diff --git a/Documentation/Doxygen/UserManual/Applications.dox b/Documentation/Doxygen/UserManual/Applications.dox index 87a95da7b8..fc0c286f5d 100644 --- a/Documentation/Doxygen/UserManual/Applications.dox +++ b/Documentation/Doxygen/UserManual/Applications.dox @@ -1,30 +1,30 @@ /** \page ApplicationsPage Using MITK and Applications Available sections: - \ref ApplicationsPageUsingMITK - \ref ApplicationsPageApplications - \ref ApplicationsPageApplicationsList \section ApplicationsPageUsingMITK Using MITK Many of the applications created with the use of MITK share common basic functionalities. Due to this, there is one manual which explains the basic usage of MITK. For more information on the use of the advanced features of an application please take a look the \ref ApplicationsPageApplicationsList , whereas if you are interested in a certain view further information can be found in \ref ModuleListPage . The basic usage information on MITK can be found in \subpage MITKUserManualPage . \section ApplicationsPageApplications What are Applications? Applications are executables, which contain a certain configuration of views and perspectives. Usually they are aimed at a selective audience or solving a particular problem. As such they focus on certain capabilities of MITK, while ignoring others. The main reason for this is to supply the users of the application with the power of MITK for solving their tasks, without daunting them with an overwhelming number of menus and options. At the same time, this allows, together with the use of perspectives, the creation of sleek and elegant workflows, which are easily comprehensible. A typical example of this would be an application which contains only views related to the analysis of the human brain (particular question) or one which contains only what is necessary for displaying medical data in the classroom (specific audience). \section ApplicationsPageApplicationsList List of Applications If you are interested in using a specific application, currently developed by the MITK team you might want to take a look first at the \ref MITKUserManualPage . Further information on any application can be found here: */ \ No newline at end of file diff --git a/Documentation/Doxygen/UserManual/ModuleList.dox b/Documentation/Doxygen/UserManual/ModuleList.dox index 7e7c4e61f1..de720208ee 100644 --- a/Documentation/Doxygen/UserManual/ModuleList.dox +++ b/Documentation/Doxygen/UserManual/ModuleList.dox @@ -1,34 +1,34 @@ /** \page ModuleListPage MITK Modules \section ModuleListPageOverview Overview The modules and bundles provide much of the extended functionality of MITK. Each encapsulates a solution to a problem and associated features. This way one can easily assemble the necessary capabilites for a workflow without adding a lot of bloat, by combining modules as needed. The distinction between developer and end user use is for convenience only and mainly distinguishes which group a module is primarily aimed at. \section ModuleListPageEndUserModuleList List of Modules for End User Use - \li \subpage org_basicimageprocessing - \li \subpage org_datamanager - \li \subpage org_diffusion + \li \subpage org_mitk_views_basicimageprocessing + \li \subpage org_mitk_views_datamanager + \li \subpage org_mitk_gui_qt_diffusionimaging \li \subpage IGTGeneralModulePage - \li \subpage org_imagecropper - \li \subpage org_imagenavigator - \li \subpage org_measurementtoolbox - \li \subpage org_moviemaker - \li \subpage org_meshdecimation - \li \subpage org_pointsetinteraction - \li \subpage RegistrationModuleOverviewPage - \li \subpage org_segment - \li \subpage org_volvis + \li \subpage org_mitk_views_imagecropper + \li \subpage org_mitk_views_imagenavigator + \li \subpage org_mitk_gui_qt_measurementtoolbox + \li \subpage org_mitk_views_moviemaker + \li \subpage org_mitk_views_meshdecimation + \li \subpage org_mitk_views_pointsetinteraction + \li \subpage org_mitk_gui_qt_registration + \li \subpage org_mitk_views_segmentation + \li \subpage org_mitk_views_volumevisualization \section ModuleListPageDevModuleList List of Modules for Developer Use and Examples \li \subpage org_surfacematerialeditor \li \subpage org_toftutorial - \li \subpage org_mitkexamples + \li \subpage org_mitk_gui_qt_examples \li \subpage org_mitkexamplesopencv */ diff --git a/Plugins/org.mitk.gui.qt.basicimageprocessing/documentation/UserManual/QmitkBasicImageProcessing.dox b/Plugins/org.mitk.gui.qt.basicimageprocessing/documentation/UserManual/QmitkBasicImageProcessing.dox index 7819419008..6fd28a3e43 100644 --- a/Plugins/org.mitk.gui.qt.basicimageprocessing/documentation/UserManual/QmitkBasicImageProcessing.dox +++ b/Plugins/org.mitk.gui.qt.basicimageprocessing/documentation/UserManual/QmitkBasicImageProcessing.dox @@ -1,132 +1,132 @@ /** -\page org_basicimageprocessing The Basic Image Processing Module +\page org_mitk_views_basicimageprocessing The Basic Image Processing Module \image html ImageProcessing_48.png "Icon of the Module" \section QmitkBasicImageProcessingUserManualSummary Summary This module provides an easy interface to fundamental image preprocessing and enhancement filters. It offers filter operations on 3D and 4D images in the areas of noise suppression, morphological operations, edge detection and image arithmetics, as well as image inversion and downsampling. Please see \ref QmitkBasicImageProcessingUserManualDetails for more detailed information on usage and supported filters. If you encounter problems using the module, please have a look at the \ref QmitkBasicImageProcessingUserManualTrouble page. \section QmitkBasicImageProcessingUserManualDetails Details Manual sections: - \ref QmitkBasicImageProcessingUserManualOverview - \ref QmitkBasicImageProcessingUserManualFilters - \ref QmitkBasicImageProcessingUserManualUsage - \ref QmitkBasicImageProcessingUserManualTrouble \section QmitkBasicImageProcessingUserManualOverview Overview This module provides an easy interface to fundamental image preprocessing and image enhancement filters. It offers a variety of filter operations in the areas of noise suppression, morphological operations, edge detection and image arithmetics. At the moment, the module can be used with all 3D and 4D image types loadable by MITK. 2D image support will be added in the future. All filters are encapsulated from the Insight Segmentation and Registration Toolkit (ITK, www.itk.org). \image html BIP_Overview.png "MITK with the Basic Image Processing module" This document will tell you how to use this module, but it is assumed that you already know how to use MITK in general. \section QmitkBasicImageProcessingUserManualFilters Filters This section will not describe the fundamental functioning of the single filters in detail, though. If you want to know more about a single filter, please have a look at http://www.itk.org/Doxygen316/html/classes.html or in any good digital image processing book. For total denoising filter, please see Tony F. Chan et al., "The digital TV filter and nonlinear denoising". Available filters are:

\a Single image operations

\a Dual image operations

\section QmitkBasicImageProcessingUserManualUsage Usage All you have to do to use a filter is to: A busy cursor appeares; when it vanishes, the operation is completed. Your filtered image is displayed and selected for further processing. (If the checkbox "Hide original image" is not selected, you will maybe not see the filter result imideately, because your filtered image is possibly hidden by the original.) For two image operations, please make sure that the correct second image is selected in the drop down menu, and the image order is correct. For sure, image order only plays a role for image subtraction and division. These are conducted (Image1 - Image2) or (Image1 / Image2), respectively. Please Note: When you select a 4D image, you can select the time step for the filter to work on via the time slider at the top of the GUI. The 3D image at this time step is extracted and processed. The result will also be a 3D image. This means, a true 4D filtering is not yet supported. \section QmitkBasicImageProcessingUserManualTrouble Troubleshooting I get an error when using a filter on a 2D image.
2D images are not yet supported... I use a filter on a 4D image, and the output is 3D.
When you select a 4D image, you can select the time step for the filter to work on via the time slider at the top of the GUI. The 3D image at this time step is extracted and processed. The result will also be a 3D image. This means, a true 4D filtering is not supported by now. A filter crashes during execution.
Maybe your image is too large. Some filter operations, like derivatives, take a lot of memory. Try downsampling your image first. All other problems.
Please report to the MITK mailing list. See http://www.mitk.org/wiki/Mailinglist on how to do this. */ diff --git a/Plugins/org.mitk.gui.qt.datamanager/documentation/UserManual/QmitkDatamanager.dox b/Plugins/org.mitk.gui.qt.datamanager/documentation/UserManual/QmitkDatamanager.dox index 17029566e0..8d95aeb632 100644 --- a/Plugins/org.mitk.gui.qt.datamanager/documentation/UserManual/QmitkDatamanager.dox +++ b/Plugins/org.mitk.gui.qt.datamanager/documentation/UserManual/QmitkDatamanager.dox @@ -1,108 +1,108 @@ /** -\page org_datamanager The DataManager +\page org_mitk_views_datamanager The DataManager \image html DataManager_48.png "Icon of the Module" \section QmitkDataManagerIntroduction Introduction The Datamanager is the central componenent to manage medical data like images, surfaces, etc.. After loading one or more data into the Datamanager the data are shown in the four-view window, the so called Standard View. The user can now start working on the data by just clicking into the standard view or by using the MITK-modules such as "Segmentation" or "Basic Image Processing". Available sections: - \ref QmitkDataManagerIntroduction - \ref QmitkDataManagerLoading - \ref QmitkDataManagerSaving - \ref QmitkDataManagerProperties - \ref QmitkDataManagerPropertiesList - \ref QmitkDataManagerPropertiesVisibility - \ref QmitkDataManagerPropertiesRepresentation - \ref QmitkDataManagerPropertiesPreferences - \ref QmitkDataManagerPropertyList \image html Overview.png "How MITK looks when starting" \section QmitkDataManagerLoading Loading Data There are three ways of loading data into the Datamanager as so called Data-Elements. The user can just drag and drop data into the Datamanager or directly into one of the four parts of the Standard View. He can as well use the Open-Button in the right upper corner. Or he can use the standard "File->Open"-Dialog on the top. A lot of file-formats can be loaded into MITK, for example The user can also load a series of 2D images (e.g. image001.png, image002.png ...) to a MITK 3D volume. To do this, just drag and drop one of those 2D data files into the Datamanager by holding the ALT key. After loading one or more data into the Datamanager they appear as Data-Elements in a sorted list inside the Datamanager. Data-Elements can also be sorted hierarchically as a parent-child-relation. For example after using the Segmentation-Module on Data-Element1 the result is created as Data-Element2, which is a child of Data-Element1 (see Screenshot1). The order can be changed by drag and drop. \image html Parent-Child.png "Screenshot1" The listed Data-Elements are shown in the standard view. Here the user can scale or rotate the medical objects or he can change the cutting planes of the object by just using the mouse inside this view. \section QmitkDataManagerSaving Saving Data There are two ways of saving data from the Datamanger. The user can either save the whole project with all Data-Elements by clicking on "File"->"Save Project" or he can save single Data-Elements by right-clicking->"Save", directly on a Data-Element. When saving the whole project, the sorting of Data-Elements is saved as well. By contrast the sorting is lost, when saving a single Data-Element. \section QmitkDataManagerProperties Working with the Datamanager \subsection QmitkDataManagerPropertiesList List of Data-Elements The Data-Elements are listed in the Datamanager. As described above the elements can be sorted hierarchically as a parent-child-relation. For example after using the Segmentation-Module on Data-Element1 the result is created as Data-Element2, which is a child of Data-Element1 (see Screenshot1). By drag and drop the sorting of Data-Elements and their hierarchical relation can be changed. \subsection QmitkDataManagerPropertiesVisibility Visibility of Data-Elements By default all loaded Data-Elements are visible in the standard view. The visibility can be changed by right-clicking on the Data-Element and then choosing "Toogle visibility". The box in front of the Data-Element in the Datamanager shows the visibility. A green-filled box means a visible Data-Element, an empty box means an invisible Data-Element (see Screenshot1). \subsection QmitkDataManagerPropertiesRepresentation Representation of Data-Elements There are different types of representations how to show the Data-Element inside the standard view. By right-clicking on the Data-Element all options are listed (see Screenshot2 and Screenshot 3). \image html Image_properties.png "Screenshot2: Properties for images" \image html Surface_Properties.png "Screenshot3: Properties for surfaces" \subsection QmitkDataManagerPropertiesPreferences Preferences For the datamanager there are already some default hotkeys like the del-key for deleting a Data-Element. The whole list is seen in Screenshot4. From here the Hotkeys can also be changed. The preference page is found in "Window"->"Preferences". \image html Preferences.png "Screenshot4" \section QmitkDataManagerPropertyList Property List The Property List displays all the properties the currently selected Data-Element has. Which properties these are depends on the Data-Element. Examples are opacity, shader, visibility. These properties can be changed by clicking on the appropriate field in the "value" column. \image html PropertyList.png "Screenshot5: Property List" */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkBrainNetworkAnalysis.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkBrainNetworkAnalysis.dox index 81963a008a..bc4a10931b 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkBrainNetworkAnalysis.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkBrainNetworkAnalysis.dox @@ -1,69 +1,69 @@ /** -\page org_brainnetworkanalysis The Brain Network Analysis Module +\page org_mitk_views_brainnetworkanalysis The Brain Network Analysis Module \image html QmitkBrainNetworkAnalysisViewIcon_64.png "Icon of the Module" \section QmitkBrainNetworkAnalysisUserManualSummary Summary This module can be used to create a network from a parcellation and a fiber image as well as to calculate and display network statistics. This document will tell you how to use this module, but it is assumed that you already know how to use MITK in general. Please see \ref QmitkBrainNetworkAnalysisUserManualDetails for more detailed information on usage and supported filters. If you encounter problems using the module, please have a look at the \ref QmitkBrainNetworkAnalysisUserManualTrouble page. \section QmitkBrainNetworkAnalysisUserManualDetails Details Manual sections: - \ref QmitkBrainNetworkAnalysisUserManualOverview - \ref QmitkBrainNetworkAnalysisUserManualUsage - \ref QmitkBrainNetworkAnalysisUserManualTrouble \section QmitkBrainNetworkAnalysisUserManualOverview Overview This module is currently under heavy development and as such the interface as well as the capabilities are likely to change significantly between different versions. This documentation describes the features of this current version. \image html QmitkBrainNetworkAnalysisInterface.png "The interface" \section QmitkBrainNetworkAnalysisUserManualUsage Usage To create a network select first a parcellation of the brain (e.g. as provided by freesurfer ) by CTRL+Leftclick and secondly a fiber image ( as created using tractography module). Then click on the "Create Network" button. To calculate network statistics select a network in the datamanager. At this time the following statistics are calculated for the entire network: Furthermore some statistics are calculated on a per node basis and displayed as histograms: Additionally you have the option to create artificial networks, for testing purposes. Currently choices are: \section QmitkBrainNetworkAnalysisUserManualTrouble Troubleshooting No known problems. All other problems.
Please report to the MITK mailing list. See http://www.mitk.org/wiki/Mailinglist on how to do this. */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox index 6f6906ec64..696214e6d4 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkDiffusionImagingUserManual.dox @@ -1,122 +1,122 @@ /** -\page org_diffusion MITK Diffusion Imaging (MITK-DI) +\page org_mitk_gui_qt_diffusionimaging MITK Diffusion Imaging (MITK-DI) This module provides means to diffusion weighted image reconstruction, visualization and quantification. Diffusion tensors as well as different q-ball reconstruction schemes are supported. Q-ball imaging aims at recovering more detailed information about the orientations of fibers from diffusion MRI measurements and, in particular, to resolve the orientations of crossing fibers. Available sections: - \ref QmitkDiffusionImagingUserManualIssues - \ref QmitkDiffusionImagingUserManualPreprocessing - \ref QmitkDiffusionImagingUserManualTensorReconstruction - \ref QmitkDiffusionImagingUserManualQBallReconstruction - \ref QmitkDiffusionImagingUserManualDicomImport - \ref QmitkDiffusionImagingUserManualQuantification - \ref QmitkDiffusionImagingUserManualVisualizationSettings - \ref QmitkDiffusionImagingUserManualReferences - \ref QmitkDiffusionImagingUserManualTechnicalDetail - \ref QmitkDiffusionImagingUserManualSubManuals \section QmitkDiffusionImagingUserManualIssues Known Issues \li Dicom Import: The dicom import has so far only been implemented for Siemens dicom images. MITK-DI is capable of reading the nrrd format, which is documented elsewhere [1, 2]. These files can be created by combining the raw image data with a corresponding textual header file. The file extension should be changed from *.nrrd to *.dwi or from *.nhdr to *.hdwi respectively in order to let MITK-DI recognize the diffusion related header information provided in the files. \section QmitkDiffusionImagingUserManualPreprocessing Preprocessing The preprocessing view gives an overview over the important features of a diffusion weighted image like the number of gradient directions, b-value and the measurement frame. Additionally it allows the extraction of the B0 image, reduction of gradient directions and the generation of a binary brain mask. The image volume can be modified by applying a new mesurement frame, which is useful if the measurement frame is not set correctly in the image header, or by averaging redundant gradient directions. \image html prepro1.png Preprocessing \section QmitkDiffusionImagingUserManualTensorReconstruction Tensor Reconstruction The tensor reconstruction view allows ITK based tensor reconstruction [3]. The advanced settings for ITK reconstruction let you configure a manual threshold on the non-diffusion weighted image. All voxels below this threshold will not be reconstructed and left blank. It is also possible to check for negative eigenvalues. The according voxels are also left blank. \image html tensor1.png ITK tensor reconstruction A few seconds (depending on the image size) after the reconstruction button is hit, a colored image should appear in the main window. \image html tensor4.png Tensor image after reconstruction The view also allows the generation of artificial diffusion weighted or Q-Ball images from the selected tensor image. The ODFs of the Q-Ball image are directly initialized from the tensor values and afterwards normalized. The diffusion weighted image is estimated using the l2-norm image of the tensor image as B0. The gradient images are afterwards generated using the standard tensor equation. \section QmitkDiffusionImagingUserManualQBallReconstruction Q-Ball Reconstruction The q-ball reonstruction bundle implements a variety of reconstruction methods. The different reconstruction methods are described in the following: \li Numerical: The original, numerical q-ball reconstruction presented by Tuch et al. [5] \li Standard (SH): Descoteaux's reconstruction based on spherical harmonic basis functions [6] \li Solid Angle (SH): Aganj's reconstruction with solid angle consideration [7] \li ADC-profile only: The ADC-profile reconstructed with spherical harmonic basis functions \li Raw signal only: The raw signal reconstructed with spherical harmonic basis functions \image html qballs1.png The q-ball resonstruction view B0 threshold works the same as in tensor reconstruction. The maximum l-level configures the size of the spherical harmonics basis. Larger l-values (e.g. l=8) allow higher levels of detail, lower levels are more stable against noise (e.g. l=4). Lambda is a regularisation parameter. Set it to 0 for no regularisation. lambda = 0.006 has proven to be a stable choice under various settings. \image html qballs2.png Advanced q-ball reconstruction settings This is how a q-ball image should initially look after reconstruction. Standard q-balls feature a relatively low GFA and thus appear rather dark. Adjust the level-window to solve this. \image html qballs3.png q-ball image after reconstruction \section QmitkDiffusionImagingUserManualDicomImport Dicom Import The dicom import does not cover all hardware manufacturers but only Siemens dicom images. MITK-DI is also capable of reading the nrrd format, which is documented elsewhere [1, 2]. These files can be created by combining the raw image data with a corresponding textual header file. The file extension should be changed from *.nrrd to *.dwi or from *.nhdr to *.hdwi respectively in order to let MITK-DI recognize the diffusion related header information provided in the files. In case your dicom images are readable by MITK-DI, select one or more input dicom folders and click import. Each input folder must only contain DICOM-images that can be combined into one vector-valued 3D output volume. Different patients must be loaded from different input-folders. The folders must not contain other acquisitions (e.g. T1,T2,localizer). In case many imports are performed at once, it is recommended to set the the optional output folder argument. This prevents the images from being kept in memory. \image html dicom1.png Dicom import The option "Average duplicate gradients" accumulates the information that was acquired with multiple repetitions for one gradient. Vectors do not have to be precisely equal in order to be merged, if a "blur radius" > 0 is configured. \section QmitkDiffusionImagingUserManualQuantification Quantification The quantification view allows the derivation of different scalar anisotropy measures for the reconstructed tensors (Fractional Anisotropy, Relative Anisotropy, Axial Diffusivity, Radial Diffusivity) or q-balls (Generalized Fractional Anisotropy). \image html quantification.png Anisotropy quantification \section QmitkDiffusionImagingUserManualVisualizationSettings ODF Visualization Setting In this small view, the visualization of ODFs and diffusion images can be configured. Depending on the selected image in the data storage, different options are shown here. For tensor or q-ball images, the visibility of glyphs in the different render windows (T)ransversal, (S)agittal, and (C)oronal can be configured here. The maximal number of glyphs to display can also be configured here for. This is usefull to keep the system response time during rendering feasible. The other options configure normalization and scaling of the glyphs. In diffusion images, a slider lets you choose the desired image channel from the vector of images (each gradient direction one image) for rendering. Furthermore reinit can be performed and texture interpolation toggled. This is how a visualization with activated glyphs should look like: \image html visualization3.png Q-ball image with ODF glyph visibility toggled ON \section QmitkDiffusionImagingUserManualReferences References 1. http://teem.sourceforge.net/nrrd/format.html 2. http://www.cmake.org/Wiki/Getting_Started_with_the_NRRD_Format 3. C.F.Westin, S.E.Maier, H.Mamata, A.Nabavi, F.A.Jolesz, R.Kikinis, "Processing and visualization for Diffusion tensor MRI", Medical image Analysis, 2002, pp 93-108 5. Tuch, D.S., 2004. Q-ball imaging. Magn Reson Med 52, 1358-1372. 6. Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R., 2007. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med 58, 497-510. 7. Aganj, I., Lenglet, C., Sapiro, G., 2009. ODF reconstruction in q-ball imaging with solid angle consideration. Proceedings of the Sixth IEEE International Symposium on Biomedical Imaging Boston, MA. 8. Goh, A., Lenglet, C., Thompson, P.M., Vidal, R., 2009. Estimating Orientation Distribution Functions with Probability Density Constraints and Spatial Regularity. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv LNCS 5761, 877 ff. \section QmitkDiffusionImagingUserManualTechnicalDetail Technical Information for Developers The diffusion imaging module uses additional properties beside the ones in use in other modules, for further information see \subpage DiffusionImagingPropertiesPage . \section QmitkDiffusionImagingUserManualSubManuals Manuals of componentes The MITK Diffusion tools consist of further components, which have their own documentation, see: - \li \subpage org_fiberprocessing - \li \subpage org_gibbstracking - \li \subpage org_odfdetails - \li \subpage org_pvanalysis - \li \subpage screenshot_maker - \li \subpage org_stochastictracking - \li \subpage org_ivim - \li \subpage org_brainnetworkanalysis - \li \subpage org_tractbasedspatialstatistics + \li \subpage org_mitk_views_fiberprocessing + \li \subpage org_mitk_views_gibbstracking + \li \subpage org_mitk_views_odfdetails + \li \subpage org_mitk_views_partialvolumeanalysisview + \li \subpage org_mitk_views_screenshotmaker + \li \subpage org_mitk_views_stochasticfibertracking + \li \subpage org_mitk_views_ivim + \li \subpage org_mitk_views_brainnetworkanalysis + \li \subpage org_mitk_views_tractbasedspatialstatistics */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkFiberProcessingViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkFiberProcessingViewUserManual.dox index 9c1e982b69..6f1f6d6963 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkFiberProcessingViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkFiberProcessingViewUserManual.dox @@ -1,23 +1,23 @@ /** -\page org_fiberprocessing Fiber Processing View +\page org_mitk_views_fiberprocessing Fiber Processing View This view provides everything needed to process fiber bundles. \image html fiberprocessing.png The Fiber Processing View Fiber extraction: Place ROIs in the 2D render widgets (cricles or polygons) and extract fibers from the bundle that pass through these ROIs by selecting the according ROI and fiber bundle in the datamanger and starting the extraction. The ROIs can be combined via logical operations. All fibers that pass through the thus generated composite ROI are extracted. The extraction can also be performed using 3D ROIs represented as closed surface meshes. In this extraction method, the logical operations are not implemented at the moment. The selected fiber bundle can be smoothed by interpolating the fiber points using Kochanek splines with the specified number of points per cm. If a float image with pixel values between 0 and 1 is selcted, the fiber bundle can be colored according to the pixel values. Generation of additional data from fiber bundles: \li Tract density image: generate a 2D heatmap from a fiber bundle \li Binary envelope: generate a binary image from a fiber bundle \li Fiber bundle image: generate a 2D rgba image representation of the fiber bundle \li Fiber endings image: generate a 2D binary image showing the locations of fiber endpoints \li Fiber endings pointset: generate a poinset containing the locations of fiber endpoints */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox index 433c4e7775..8424ea110f 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkGibbsTrackingViewUserManual.dox @@ -1,44 +1,44 @@ /** -\page org_gibbstracking Gibbs Tracking View +\page org_mitk_views_gibbstracking Gibbs Tracking View This view provides the user interface for the Gibbs Tracking algorithm, a global fiber tracking algorithm, originally proposed by Reisert et.al. [1]. Available sections: - \ref QmitkGibbsTrackingUserManualInputData - \ref QmitkGibbsTrackingUserManualParameters - \ref QmitkGibbsTrackingUserManualTrackingSurveillance - \ref QmitkGibbsTrackingUserManualReferences \image html gibbstrackingview.png The Gibbs Tracking View \section QmitkGibbsTrackingUserManualInputData Input Data Mandatory Input: \li One Q-Ball image selected in the datamanager Optional Input: \li Mask Image: Float image used as probability mask for the generation of fiber segments. Usually used as binary brain mask to reduce the searchspace of the algorithm and to avoid fibers resulting from noise outside of the brain. \section QmitkGibbsTrackingUserManualParameters Q-Ball Reconstruction \li Number of iterations: More iterations causes the algorithm to be more stable but also to take longer to finish the tracking. Rcommended: 10⁷-10⁹ iterations. \li Particle length/width/weight controlling the contribution of each particle to the model M \li Start and end temperature controlling how fast the process reaches a stable state. (usually no change needed) \li Weighting between the internal (affinity of the model to long and straigt fibers) and external energy (affinity of the model towards the data). (usually no change needed). \li Minimum fiber length constraint (in mm). Shorter fibers are discarded after the tracking. The automatic selection of parameters for the particle length/width and weight are determined directly from the input image using information about the image spacing and GFA. \image html gibbstrackingviewadvanced.png Advanced Tracking Parameters \section QmitkGibbsTrackingUserManualTrackingSurveillance Surveilance of the tracking process Once started, the tracking can be monitored via the textual output that informs about the tracking progress and several stats of the current state of the algorithm. If enabled, the intermediate tracking results are displayed in the renderwindows each second. This live visualization should usually be disabled for performance reasons. It can be turned on and off during the tracking process via the according checkbox. The button next to this checkbox allows the visualization of only the next iteration step. \section QmitkGibbsTrackingUserManualReferences References [1] Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., Kiselev, V.: Global fiber reconstruction becomes practical. Neuroimage 54 (2011) 955-962 */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkOdfDetailsViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkOdfDetailsViewUserManual.dox index 7885ebd5dc..f54439baa9 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkOdfDetailsViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkOdfDetailsViewUserManual.dox @@ -1,8 +1,8 @@ /** -\page org_odfdetails ODF Details View +\page org_mitk_views_odfdetails ODF Details View This view provides detailed information about the orentation distribution function at the current crosshair position (if a Tensor/Q-Ball image is selected). A visualization of the ODF as well as statistical information are displayed. \image html odfdetails.png The Gibbs Tracking View */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkPartialVolumeAnalysisViewManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkPartialVolumeAnalysisViewManual.dox index a515ad40fc..7308a7018d 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkPartialVolumeAnalysisViewManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkPartialVolumeAnalysisViewManual.dox @@ -1,23 +1,23 @@ /** -\page org_pvanalysis Partial Volume Analysis +\page org_mitk_views_partialvolumeanalysisview Partial Volume Analysis The "Partial Volume Analysis" view can be found in the "Quantification" perspective. It allows for robust quantification of diffusion or other scalar measures in the presents of two classes (e.g. fiber vs. non-fiber) and partial volume between them. The algorithm estimates a probabilistic segmentation of the three classes and returns a weighted average of the measure of interest within the each class. \image html pvanalysisview.png The Partial Volume Analysis View \section QmitkPVAAnalysisUserManualExport Export All measures are automatically written to the clipboard once the estimation is updated. The histogram export is provided by the button underneath the histogram. The values can be pasted to excel or any text editor. \section QmitkPVAAnalysisUserManualAdvancedSettings Advanced Settings Are not recommended for use yet. \section QmitkPVAAnalysisUserManualSuggestedReadings Suggested Readings Diffusion tensor imaging in primary brain tumors: reproducible quantitative analysis of corpus callosum infiltration and contralateral involvement using a probabilistic mixture model. Stieltjes B, Schlüter M, Didinger B, Weber MA, Hahn HK, Parzer P, Rexilius J, Konrad-Verse O, Peitgen HO, Essig M. Neuroimage. 2006 Jun;31(2):531-42. Epub 2006 Feb 14. PMID: 16478665 */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkStochasticTrackingViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkStochasticTrackingViewUserManual.dox index 7573738c5e..0ed40d2add 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkStochasticTrackingViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkStochasticTrackingViewUserManual.dox @@ -1,31 +1,31 @@ /** -\page org_stochastictracking Stochastic Tracking View +\page org_mitk_views_stochasticfibertracking Stochastic Tracking View This view provides the user interface for the Stochastic Fibertracking algorithm, proposed by Ngo [1]. Available sections: - \ref QmitkStochasticTrackingUserManualInputData - \ref QmitkStochasticTrackingUserManualParameters - \ref QmitkStochasticTrackingUserManualReferences \image html stochastictrackingview.png Stochastic Tracking View \section QmitkStochasticTrackingUserManualInputData Input Data Mandatory Input: \li One DWI Image image selected in the datamanager \li One or more ROIs selected in the datamanager For a successful execution of the stochastic tractography filter, a DWI input and a binary image defining the desired ROI are required. The ROI serves as the origin from where on the fibers are beeing tracked. To generate the seed ROI the segmentation view in the quantification perspective can be used or a binary image can be loaded. \section QmitkStochasticTrackingUserManualParameters Input Parameters \li Parameters such as max. tract length, number of seeds per voxel and likelihood cache size in MB can be controlled individually. \li After successfully setting necessary Input and Parameter, pressing the command button executes the algorithm. \section QmitkStochasticTrackingUserManualReferences References [1] Tri M. Ngo, Polina Golland, and Tri M. Ngo. A stochastic tractography system and applications, 2007 */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox index 26b49bbb0a..2a199fd3a9 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkTbssViewUserManual.dox @@ -1,59 +1,59 @@ /** -\page org_tractbasedspatialstatistics The TBSS Module +\page org_mitk_views_tractbasedspatialstatistics The TBSS Module \image html tbss.png "Icon of the Module" \section QmitkTractbasedSpatialStatistics Summary This module can be used to locally explore data resulting from preprocessing with the TBSS module of FSL This document will tell you how to use this module, but it is assumed that you already know how to use MITK in general and how to work with the TBSS module of FSL. If you encounter problems using the module, please have a look at the \ref QmitkTractbasedSpatialStatisticsUserManualTrouble page. Sections: - \ref QmitkTractbasedSpatialStatisticsUserManualOverview - \ref QmitkTractbasedSpatialStatisticsUserManualFSLImport - \ref QmitkTractbasedSpatialStatisticsUserManualRois - \ref QmitkTractbasedSpatialStatisticsUserManualProfiles - \ref QmitkTractbasedSpatialStatisticsUserManualTroubleshooting - \ref QmitkTractbasedSpatialStatisticsUserManualReferences \section QmitkTractbasedSpatialStatisticsUserManualOverview Overview This module is currently under development and as such the interface as well as the capabilities are likely to change significantly between different versions. This documentation describes the features of this current version. \section QmitkTractbasedSpatialStatisticsUserManualFSLImport FSL Import The FSL import allows to import data that has been preprocessed by FSL. FSL creates output images that typically have names like all_FA_skeletonized.nii.gz that are 4-dimensional images that contain registered images of all subjects. By loading this 4D image into the datamanager and listing the groups with the correct number of subjects, in the order of occurrence in the 4D image, in the TBSS-View using the Add button and clicking the import subject data a TBSS file is created that contains all the information needed for tract analysis. The diffusion measure of the image can be set as well. \image html fslimport.png "FSL Import" \section QmitkTractbasedSpatialStatisticsUserManualRois Regions of Interest (ROIs) To create a ROI the mean FA skeleton (typically called mean_FA_skeleton.nii.gz) that is created by FSL should be loaded in to the datamanager and selected. By using the Pointlistwidget points should be set on the skeleton (make sure to select points with relatively high FA values). Points are set by first selecting the button with the '+' and than shift-leftclick in the image. When the correct image is selected in the datamanager the Create ROI button is enabled. Clicking this will create a region of interest that passes through the previously selected points. The roi appears in the datamanager. Before doing so, the name of the roi and the information on the structure on which the ROI lies can be set. This will be saved as extra information in the roi-image. Before the ROI is calculated, a pop-up window will ask the user to provide a threshold value. This should be the same threshold that was previously used in FSL to create a binary mask of the FA skeleton. When this is not done correctly, the region of interest will possible contain zero-valued voxels. \image html tbssRoi.png "Regions of Interest (ROIs)" \section QmitkTractbasedSpatialStatisticsUserManualProfiles y selecting a tbss image with group information and a region of interest image (as was created in a previous stap). A profile plot is drawn in the plot canvas. By clicking in the graph the crosshairs jump to the corresponding location in the image. \image html profiles.png "Profile plots" \section QmitkTractbasedSpatialStatisticsUserManualTroubleshooting Troubleshooting Please report to the MITK mailing list. See http://www.mitk.org/wiki/Mailinglist on how to do this. \section QmitkTractbasedSpatialStatisticsUserManualReferences References 1. S.M. Smith, M. Jenkinson, H. Johansen-Berg, D. Rueckert, T.E. Nichols, C.E. Mackay, K.E. Watkins, O. Ciccarelli, M.Z. Cader, P.M. Matthews, and T.E.J. Behrens. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage, 31:1487-1505, 2006. 2. S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkUserIVIMViewManual.dox b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkUserIVIMViewManual.dox index 1ed8f2af10..b46d32f9dd 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkUserIVIMViewManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimaging/documentation/UserManual/QmitkUserIVIMViewManual.dox @@ -1,41 +1,41 @@ /** -\page org_ivim Intra-voxel incoherent motion estimation (IVIM) +\page org_mitk_views_ivim Intra-voxel incoherent motion estimation (IVIM) The required input for the "Intra-voxel incoherent motion estimation" (IVIM) is a diffusion weighted image (.dwi or .hdwi) that was acquired with several different b-values. \image html ivimview.png The IVIM View Once an input image is selected in the datamanager, the IVIM view allows for interactive exploration of the dataset (click around in the image and watch the estimated parameters in the figure of the view) as well as generation of f-, D-, and D*-maps (activate the checkmarks and press "Generate Output Images"). The "neglect b<" threshold allows you to ignore b-values smaller then a threshold for the initial fit of f and D. D* is then estimated using all measurements. The exact values of the current fit are always given in the legend underneath the figure. \section QmitkDiffusionImagingUserManualInputData Region of interest analysis Create region of interest: To create a new segmentatin, open the "quantification" perspective, select the tab "Segmentation", and create a segmentation of the structure of interest. Alternatively, of course, you may also load a binary image from file or generate your segmentation in any other possible way. IVIM in region of interset: Go back to the "IVIM" perspective and select both, the diffusion image and the segmentation (holding the CTRL key). A red message should appear "Averaging N voxels". \section QmitkDiffusionImagingUserManualInputData Export All model parameters and corresponding curves can be exported to clipboard using the buttons underneath the figure. \section QmitkDiffusionImagingUserManualInputData Advanced Settings Advanced users, that know what they are doing, can change the method for the model-fit under "Advanced Settings" on the very bottom of the view. 3-param fit, linear fit of f/D, and fix D* are among the options. \section QmitkDiffusionImagingUserManualInputData Suggested Readings Toward an optimal distribution of b values for intravoxel incoherent motion imaging. Lemke A, Stieltjes B, Schad LR, Laun FB. Magn Reson Imaging. 2011 Jul;29(6):766-76. Epub 2011 May 5. PMID: 21549538 Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. Lemke A, Laun FB, Klauss M, Re TJ, Simon D, Delorme S, Schad LR, Stieltjes B. Invest Radiol. 2009 Dec;44(12):769-75. PMID: 19838121 */ diff --git a/Plugins/org.mitk.gui.qt.diffusionimagingapp/documentation/UserManual/QmitkDiffusionImagingAppUserManual.dox b/Plugins/org.mitk.gui.qt.diffusionimagingapp/documentation/UserManual/QmitkDiffusionImagingAppUserManual.dox index 65b8a2a24b..9f679c8296 100644 --- a/Plugins/org.mitk.gui.qt.diffusionimagingapp/documentation/UserManual/QmitkDiffusionImagingAppUserManual.dox +++ b/Plugins/org.mitk.gui.qt.diffusionimagingapp/documentation/UserManual/QmitkDiffusionImagingAppUserManual.dox @@ -1,10 +1,10 @@ /** -\page org_diffusionapplication Using The Diffusion Imaging Application +\page org_mitk_gui_qt_diffusionimagingapp Using The Diffusion Imaging Application \section QMitkDiffusionApplicationManualOverview What is the Diffusion Imaging Application The Diffusion Imaging Application contains selected views for the analysis of images of the human brain. These encompass the views developed by the Neuroimaging Group of the Division Medical and Biological Informatics as well as basic image processing views such as segmentation and volumevisualization. For a basic guide to MITK see \ref MITKUserManualPage . */ diff --git a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/MITKExamples.dox b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/MITKExamples.dox index 512a525dcd..bf7631f9a6 100644 --- a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/MITKExamples.dox +++ b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/MITKExamples.dox @@ -1,18 +1,18 @@ /** -\page org_mitkexamples Examples for the use of MITK +\page org_mitk_gui_qt_examples Examples for the use of MITK \section QmitkExamplesUserManualSummary Summary This module is a collection of examples for developing with mitk. The following examples are included: */ diff --git a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkColourImageProcessing.dox b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkColourImageProcessing.dox index 4ba970890f..dea970f477 100644 --- a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkColourImageProcessing.dox +++ b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkColourImageProcessing.dox @@ -1,34 +1,34 @@ /** -\page org_colourimageprocessing The Colour Image Processing Module +\page org_mitk_views_colourimageprocessing The Colour Image Processing Module \image html ColorImageProcessing.png "Icon of the Module" \section QmitkColourImageProcessingUserManualSummary Summary This module allows the user to create coloured images from medical images. Generally, these coloured images would be used in presentations, research papers, or as a teaching aide. \section QmitkColourImageProcessingUserManualOverview Overview The purpose of this module is to create coloured images, which can then be later used in presentations, research papers, or anything else the user desires. Furthermore, different body sections can be assigned a different colour. These images are not particularly useful for further image processing, but provide nice, clear, diagrams. Please note that ultrasound images cannot be coloured. \section QmitkColourImageProcessingUserManualFilters Creating Coloured Images Open an image in mitk, then click on the colour wheel button. Make sure the image you loaded is selected.

\ Image -> RGBAimage

Clicking on this button creates an RGBA image from your medical image, which should appear in your data manager.

\ Image + mask -> RGBAimage

Before clicking on this button, a mask is needed. A mask is generally created using the segmentation tool. Make sure both the mask and image are selected, then click on the image + mask -> RGBA image button. This creates an RGBA image that is coloured only in section defined by the mask.

\ Image + mask + color-> RGBAimage

This functions the same way as the Image + mask -> RGBAimage. The difference is that the user can select the colour of the section represented by the mask. This is done by clicking on the colour next to the Image + mask + color-> RGBAimage button If multiple RGBA images are created with different colours, they can be combined into one image. Ensure that the desired coloured images are selected, then click on the combine RGBA images button. */ diff --git a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkIsoSurfaceUserManual.dox b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkIsoSurfaceUserManual.dox index 214e858245..1e8bb8c1f5 100644 --- a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkIsoSurfaceUserManual.dox +++ b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkIsoSurfaceUserManual.dox @@ -1,37 +1,37 @@ /** -\page org_isosurface The Iso Surface Module +\page org_mitk_views_isosurface The Iso Surface Module \image html IsoSurfaceIcon.png "Icon of the Module" Available sections: - \ref QmitkIsoSurfaceUserManualOverview - \ref QmitkIsoSurfaceUserManualFeatures - \ref QmitkIsoSurfaceUserManualUsage - \ref QmitkIsoSurfaceUserManualTroubleshooting \section QmitkIsoSurfaceUserManualOverview Overview IsoSurface is a program module for creating surfaces (e.g. polygon structures) out of images. The user defines a threshold that seperates object and background inside the image. Pixels that belong to the object need grey values below the threshold. Pixles with grey values above the threshold will be ignored. The result is a polygon object that can be saved as an *.stl-object. \section QmitkIsoSurfaceUserManualFeatures Features - Creates a surface by thresholding an image \section QmitkIsoSurfaceUserManualUsage Usage How to create a surface: - Load an image into the program, for example by drag & drop - Look for a meaningful threshold. All pixel grey values of the image that are lower than the threshold will be used to create the surface. All grey values that are higher than the surface will be ignored. You can find the best threshold by using the Volumetry-Functionality or by reading the grey value while clicking on a pixel (see picture 2). - Insert the threshold into the GUI - Press the Button "Create Surface" \image html IsoSurfaceGUI.png "Graphical User Interface of Iso Surface" \section QmitkIsoSurfaceUserManualTroubleshooting Troubleshooting */ diff --git a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkRegionGrowingUserManual.dox b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkRegionGrowingUserManual.dox index 81c517db55..97360b23ea 100644 --- a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkRegionGrowingUserManual.dox +++ b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkRegionGrowingUserManual.dox @@ -1,29 +1,29 @@ /** -\page org_regiongrowing The Region Growing Module +\page org_mitk_views_regiongrowing The Region Growing Module \image html regiongrowing.png "Icon of the Module" Available documentation sections: - \ref QmitkRegionGrowingUserManualOverview - \ref QmitkRegionGrowingUserManualUsage \section QmitkRegionGrowingUserManualOverview Overview The Region growing module provides a programming example, showing developers how to create new bundles for MITK with a graphical user interface (GUI) that also uses some ITK image filters. For the programmers: this functionality is the result of tutorial step 9 \section QmitkRegionGrowingUserManualUsage Usage */ diff --git a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleExampleUserManual.dox b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleExampleUserManual.dox index a501bb12c1..12df11bc6d 100644 --- a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleExampleUserManual.dox +++ b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleExampleUserManual.dox @@ -1,21 +1,21 @@ /** -\page org_simpleexample The Simple Example module +\page org_mitk_views_simpleexample The Simple Example module \image html SimpleExample.png "Icon of the Module" \section QmitkSimpleExampleViewUserManualSummary Summary This module is namely a simple example for simple image interaction. It offers: */ diff --git a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleMeasurementUserManual.dox b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleMeasurementUserManual.dox index 72d0a53688..f3aadf2c81 100644 --- a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleMeasurementUserManual.dox +++ b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/QmitkSimpleMeasurementUserManual.dox @@ -1,44 +1,44 @@ /** -\page org_simplemeasurement The Simple Measurement Module +\page org_mitk_views_simplemeasurement The Simple Measurement Module \image html SimpleMeasurementIcon.png "Icon of the Module" Available sections: - \ref QmitkSimpleMeasurementUserManualOverview - \ref QmitkSimpleMeasurementUserManualFeatures - \ref QmitkSimpleMeasurementUserManualUsage \section QmitkSimpleMeasurementUserManualOverview Overview SimpleMeasurement is a program module that allows to measure distances, angles and paths on a dataset. \section QmitkSimpleMeasurementUserManualFeatures Features \section QmitkSimpleMeasurementUserManualUsage Usage To use the SimpleMeasurement Module, a data set must first be loaded. This can be done by drag & drop. Choose the simplemeasurement method you need by pressing the according button. What the different modes mean and how to use them: \image html SimpleMeasurementGUI.png Graphical User Interface of SimpleMeasurement */ diff --git a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/org_mitk_gui_qt_viewinitialization.dox b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/org_mitk_gui_qt_viewinitialization.dox index 11f97bd1d3..f192b11dd4 100644 --- a/Plugins/org.mitk.gui.qt.examples/documentation/Manual/org_mitk_gui_qt_viewinitialization.dox +++ b/Plugins/org.mitk.gui.qt.examples/documentation/Manual/org_mitk_gui_qt_viewinitialization.dox @@ -1,8 +1,8 @@ /** -\page org_viewinitialitzation The View Initialization Module +\page org_mitk_views_viewinitialitzation The View Initialization Module \image html viewInitializationIcon.png "Icon of the Module" This view serves as a sandbox for understanding and experimenting with the geometry initialization parameters. For example, to view the axial slices from above instead of from below. It is not intended for end users, but for developers. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/Manual.dox index 52f21a462b..6ac68844cb 100644 --- a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/Manual.dox @@ -1,15 +1,15 @@ /** \page org_mitk_gui_qt_igttracking IGT Tracking This bundle offers basic tracking functionalities. This includes connecting to a tracking system, logging and recording of tracking data, managing tracking tools and playing recorded tracking data. The bundle includes different views, which are described in different pages in detail: */ diff --git a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTNavigationToolManager.dox b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTNavigationToolManager.dox index 1fee58aeee..2253f1917f 100644 --- a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTNavigationToolManager.dox +++ b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTNavigationToolManager.dox @@ -1,47 +1,47 @@ /** -\page org_igtnavigationtoolmanager The MITK-IGT Navigation Tool Manager +\page org_mitk_views_igtnavigationtoolmanager The MITK-IGT Navigation Tool Manager \image html iconNavigationToolManager.png "Icon of the Module" \section QmitkMITKIGTNavigationToolManager Introduction This bundle allows for creating and editing NavigationToolStorages. These storages contains naviagtion tools of a tracking device, can be saved permanently and used later for any other IGT application. Available sections: - \ref QmitkMITKIGTNavigationToolManager - \ref QmitkMITKIGTNavigationToolManagerToolOverview - \ref QmitkMITKIGTNavigationToolManagerManagingNavigationToolStorage - \ref QmitkMITKIGTNavigationToolManagerAddingEditingNavigationTools \section QmitkMITKIGTNavigationToolManagerToolOverview Navigation Tools Overview A navigation tool of MITK-IGT represents a tracking tool (e.g. an emt sensor or an optically tracked tool) and it's corresponding data, like it's name and it's surface. A navigation tool is a generalized container for any trackable object in combination with it's additional information. Every navigation tool has different properties which are: Note that not all properties are needed for all types of tools. A tool definition file, for example, is only needed by optical tracking tools (e.g. a .rom file for Polaris or a toolfile for the MicronTracker). A tool associated with the aurora system is alwalys identified by it's serial number. You can also detect Aurora tools automatically with the TrackingToolbox bundle and edit the automatically detected tool storage later with this bundle. \section QmitkMITKIGTNavigationToolManagerManagingNavigationToolStorage Managing Navigation Tool Storage In order to create a new storage container, or edit an existing one, you can use the buttons "add", "edit" and "delete" to manage the contained navigation tools. If you click "edit" or "delete" the operation is applied on the currently selected tool, as shown in the screenshot below. If you want to create a new storage container, just start adding tools when you start the program and save the storage container. To edit an existing tool storage container click "load" and add/edit/delete tools. \image html NavigationToolManagemantStartScreen.png "Screenshot of the main view of NavigationToolManagent" \section QmitkMITKIGTNavigationToolManagerAddingEditingNavigationTools Adding / Editing Navigation Tools If you add or edit a navigation tool, an input mask, as shown in the screenshot below, appears. The tool identifier is filled automatically, if you change it, remember that it is unique in the current storage. Also, choose a valid surface for every tool, this is nessesary for correct tool visualization. The other information depends on the tracking system type. So choose a tool file for the Polaris or the MicronTracker system and type in a serial number for the Aurora system. Two identical tools with the same serial number are also possible, they are assigned by the order in which they are attached to the device. As long as they also have the same surface, this should not be a problem. The tool type is additional information which is not needed by the tracking device but might be needed by further IGT applications. \image html NavigationToolManagementAddTool.png "Screenshot of add/edit navigation tool screen" */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTTrackingToolbox.dox b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTTrackingToolbox.dox index 82a85e61fa..c6d911fe51 100644 --- a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTTrackingToolbox.dox +++ b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkMITKIGTTrackingToolbox.dox @@ -1,64 +1,64 @@ /** -\page org_igttrackingtoolbox The MITK-IGT Tracking Toolbox +\page org_mitk_views_igttrackingtoolbox The MITK-IGT Tracking Toolbox \image html iconTrackingToolbox.png "Icon of the module" Available sections: - \ref QmitkMITKIGTTrackingToolboxIntroduction - \ref QmitkMITKIGTTrackingToolboxWorkflow - \ref QmitkMITKIGTTrackingToolboxConnecting - \ref QmitkMITKIGTTrackingToolboxLoadingTools - \ref QmitkMITKIGTTrackingToolboxAutoDetection - \ref QmitkMITKIGTTrackingToolboxStartTracking - \ref QmitkMITKIGTTrackingToolboxLogging - \ref QmitkMITKIGTTrackingOptions \section QmitkMITKIGTTrackingToolboxIntroduction Introduction The MITK-IGT Tracking Toolbox is a bundle which allows you to connect to a tracking device, track and visualize navigation tools and write the tracked data into a log file. Currently the devices Polaris, Aurora (both Northern Digital Inc. (NDI); Waterloo, Ontario, Canada) and MicronTracker (Claron Technology, Inc.; Toronto, Ontario, Canada) are supported. The MicroBird family (Ascension Technology Corporation, Inc.; Burlington, USA) will hopefully follow soon since it is already supported by the tracking layer of IGT. The logging feature of the Tracking Toolbox supports logging in XML or CSV format. \image html screenshot_mitk.png "MITK Screenshot with the TrackingToolbox activated" \section QmitkMITKIGTTrackingToolboxWorkflow General workflow Introduction A general Workflow with the Tracking Toolbox may be: \section QmitkMITKIGTTrackingToolboxConnecting Tracking Device Configuration The tracking device can be specified in the tracking device configuration section located in the upper area of the tracking tab. As shown in the screenshot below, you choose your tracking device in the drop down menu. If you use a tracking system connected to a serial port, like Aurora or Polaris, you then need to specifiy the serial port. In case of the MicronTracker you only need to ensure that all drivers are installed correctly and integrated into MITK. If you want to check the connection, press "test connection". The results are displayed in the small black box on the right. \image html configurationWidget.png "Tracking Device Configuration" \section QmitkMITKIGTTrackingToolboxLoadingTools Loading tools To load tools which can be tracked you need a predefined tracking tool storage. If you use the Aurora system you also have the possibility to automatically detect the connected tools. In this case a tracking tool storage is created by the software (see section below). Otherwise you can use the MITK bundle NavigationToolManager to define a navigation tool storage. There you can create navigation tools with the corresponding toolfiles, visualization surfaces and so on. Please see NavigationToolManager manual for more details. Navigation tool storages can be loaded by pressing the button "Load Tools". Please ensure that the tracking device type of the tools matches the chosen tracking device, otherwise you will get an error message if you try to start tracking. All loaded tools will then be displayed in grey as shown in the screenshot below. If you start tracking they will become green if the tools were found and red if they were not found inside the tracking volume. \image html trackingToolsWidget.png "Tracking Tools" \section QmitkMITKIGTTrackingToolboxAutoDetection Auto detection of tools (only Aurora) If the Aurora tracking system is used, a button "Auto Detection" appears. If you press this button the software connects to the system and automatically detects all connected tools. You will then be asked whether you want to save the detected tools as a tool storage to the hard drive. You might want to do this if you want to use or modify this tool storage later. In the automatically detected tool storage the tools are named AutoDetectedTools1, AutoDetectedTools2, and so on. Small spheres are used as tool surfaces. After autodetection the detected tools are loaded automatically even if you did not save them. \section QmitkMITKIGTTrackingToolboxStartTracking Start/stop tracking Tracking can simply be started by pressing "Start Tracking". Note that options may not be changed during tracking. Once tracking has started the tracking volume (only if the option is on) and the tools are visualized in the 3D view of MITK. \section QmitkMITKIGTTrackingToolboxLogging Logging features If your device is tracking, you are able to log the tracking data by using the logging tab. You first must define a file name. You can then choose whether you want comma seperated (csv) or xml format. Press "Start Logging" to start logging. You can also limit the number of logged frames, which will cause the logging to stop automatically after the given number. \section QmitkMITKIGTTrackingOptions Options In the options tab you can enable or disable the visualization of the tracking volume and of the tool quaternions. If enabled, the tool quaternions are shown in the tool information. You can also define the update rate of the tracking data. The update rate should not be set higher than the update rate of the tracking system. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkNavigationDataPlayer.dox b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkNavigationDataPlayer.dox index 667946ac47..c6dec7930e 100644 --- a/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkNavigationDataPlayer.dox +++ b/Plugins/org.mitk.gui.qt.igttracking/documentation/UserManual/QmitkNavigationDataPlayer.dox @@ -1,18 +1,18 @@ /** -\page org_navigationdataplayer NavigationData Player +\page org_mitk_views_navigationdataplayer NavigationData Player \image html iconNavigationDataPlayer.png "Icon of NavigationData Player" Available sections: - \ref NavigationDataPlayerOverview \section NavigationDataPlayerOverview The navigation data player plays recorded or artificial navigation data of one ore more tracking tools and visualizes their trajectory. For that purpose select an input file (*.xml only) and select which tracking tool's trajectory should be visualized. If you additionally activate the checkbox "Splines" the trajectory curve will be smoothed via spline interpolation. Press the button "start" for starting the player and the visualization. If "Sequential Mode" is checked, the navigation data are played sequentially without regarding the recorded time steps. You can use the resolution field to define which part of the samples you want to use. E.g. 1 = every sample; 2 = every second sample; 3 = every third sample, ... \image html screenshotNavigationDataPlayer.png "Screenshot of the NavigationData Player" */ diff --git a/Plugins/org.mitk.gui.qt.imagecropper/documentation/UserManual/QmitkImageCropperUserManual.dox b/Plugins/org.mitk.gui.qt.imagecropper/documentation/UserManual/QmitkImageCropperUserManual.dox index 0c7dbce73a..58b7015c0e 100644 --- a/Plugins/org.mitk.gui.qt.imagecropper/documentation/UserManual/QmitkImageCropperUserManual.dox +++ b/Plugins/org.mitk.gui.qt.imagecropper/documentation/UserManual/QmitkImageCropperUserManual.dox @@ -1,41 +1,41 @@ /** -\page org_imagecropper The Image Cropper Module +\page org_mitk_views_imagecropper The Image Cropper Module \image html icon.png "Icon of the Module" Available sections: - \ref QmitkImageCropperUserManualOverview - \ref QmitkImageCropperUserManualFeatures - \ref QmitkImageCropperUserManualUsage - \ref QmitkImageCropperUserManualTroubleshooting \section QmitkImageCropperUserManualOverview Overview ImageCropper is a functionality which allows the user to manually crop an image by means of a bounding box. The functionality does not create a new image, it only hides parts of the original image. \section QmitkImageCropperUserManualFeatures Features - Crop a selected image using a bounding box. - Set the border voxels to a specific user defined value after cropping. \section QmitkImageCropperUserManualUsage Usage First select from the drop down menu the image to crop. The three 2D widgets show yellow rectangles representing the bounding box in each plane (axial, sagital, coronal), the lower right 3D widget shows the entire volume of the bounding box.\n - To change the size of bounding box press control + right click and move the cursor up/down or left/right in one of the three 2D views.\n - To change the orientation of the bounding box press control + middle click and move the cursor up/down or left/right in one of the three 2D views.\n - To move the bounding box press control + left click and move the cursor to the wanted position in one of the three 2D views.\n To show the result press the [crop] button.\n To crop the image again press the [New bounding box!] button.\n\n All actions can be undone by using the global undo function (Ctrl+Z).\n To set the border voxels to a specific value after cropping the image, activate the corresponding checkbox and choose a gray value. \section QmitkImageCropperUserManualTroubleshooting Troubleshooting */ diff --git a/Plugins/org.mitk.gui.qt.imagenavigator/documentation/UserManual/QmtikImageNavigator.dox b/Plugins/org.mitk.gui.qt.imagenavigator/documentation/UserManual/QmtikImageNavigator.dox index 721f5b8d33..86ed33d160 100644 --- a/Plugins/org.mitk.gui.qt.imagenavigator/documentation/UserManual/QmtikImageNavigator.dox +++ b/Plugins/org.mitk.gui.qt.imagenavigator/documentation/UserManual/QmtikImageNavigator.dox @@ -1,15 +1,15 @@ /** -\page org_imagenavigator The Image Navigator +\page org_mitk_views_imagenavigator The Image Navigator \image html Slider.png "Icon of the Module" \image html ImageNavigator.png "Image Navigator" Fast movement through the available data can be achieved by using the Image Navigator. By moving the sliders around you can scroll quickly through the slides and timesteps. By entering numbers in the relevant fields you can jump directly to your point of interest. The "Show detail" checkbox enables you to see the world coordinates in millimetres and the index/voxel coordinates. These may be edited to jump to a specific location. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkImageStatistics.dox b/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkImageStatistics.dox index d8b1a02745..95b83862ae 100644 --- a/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkImageStatistics.dox +++ b/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkImageStatistics.dox @@ -1,44 +1,44 @@ /** -\page org_imagestatistics The Image Statistics Module +\page org_mitk_views_imagestatistics The Image Statistics Module \image html ImageStatistic_48.png "Icon of the Module" \section QmitkImageStatisticsUserManualSummary Summary This module provides an easy interface to quickly compute some features of a whole image or a region of interest. This document will tell you how to use this module, but it is assumed that you already know how to use MITK in general. Please see \ref QmitkImageStatisticsUserManualDetails for more detailed information on usage and supported filters. If you encounter problems using the module, please have a look at the \ref QmitkImageStatisticsUserManualTrouble page. \section QmitkImageStatisticsUserManualDetails Details Manual sections: - \ref QmitkImageStatisticsUserManualOverview - \ref QmitkImageStatisticsUserManualUsage - \ref QmitkImageStatisticsUserManualTrouble \section QmitkImageStatisticsUserManualOverview Overview This module provides an easy interface to quickly compute some features of a whole image or a region of interest. \image html Screenshot1.png "The interface" \section QmitkImageStatisticsUserManualUsage Usage After selection of an image or a binary mask of an image in the datamanader, the Image Statistics module shows some statistical information. If a mask is selected, the name of the mask and the name of the image, to which the mask is applied, are shown at the top. Below it is the statistics window which displays the calculated statistical features (such as mean, standard deviation...) and the histogram. At the bottom of the module are two buttons. They copy their respective data in csv format to the clipboard. \section QmitkImageStatisticsUserManualTrouble Troubleshooting No known problems. All other problems.
Please report to the MITK mailing list. See http://www.mitk.org/wiki/Mailinglist on how to do this. */ diff --git a/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurement.dox b/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurement.dox index c67288184b..ad3e18a4d6 100644 --- a/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurement.dox +++ b/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurement.dox @@ -1,134 +1,134 @@ /** -\page org_measurement The Measurement Module +\page org_mitk_views_measurement The Measurement Module \image html Measurement_48.png "Icon of the Module" \section QmitkMeasurementUserManualOverview Overview The Measurement module enables the user to interact with 2D images or single slices of 3D image stacks and planar figure data types. It allows to measure distances, angels, pathes and several geometric figures on a dataset. Available Sections: - \ref QmitkMeasurementUserManualOverview - \ref QmitkMeasurementUserManualFeatures - \ref SubOne - \ref SubTwo - \ref SubThree - \ref SubFour - \ref SubFive - \ref SubSix - \ref SubSeven - \ref QmitkMeasurementUserManualUsage - \ref One - \ref Two - \ref Three - \ref Four The workflow to use this module is: \image html Workflow.png The workflow is repeatedly useable with the same or different measurement figures, which are correlated to the choosen image and can be saved together with it for future use. On pressing the Measurement icon (see picture below the page title) in the view button line the basic appearance of the bundle is as follws. \image html Basic_Screen_edited.JPG The standard working plane is "Axial" but the other standard viewplanes ("Saggital" and "Coronal") are also valid for measurements. To swap between the view planes refer to 3M Application Bundle User Manual. \section QmitkMeasurementUserManualFeatures Features The bundle as it is depicted below offers the following features in the order of apperance on the image from top to bottom: \image html Measurement_View.JPG The first information is the selected image's name (here: DICOM-MRI-Image) followed by the measurement figures button line with the seven measurement figures. From left to right the buttons are connected with the following functions: \subsection SubOne Draw Line Draws a line between two set points and returns the distance between these points. \subsection SubTwo Draw Path Draws a path between several set points (two and more) and calculates the circumference, that is all line's length summed up. Add the final point by double left click. \subsection SubThree Draw Angle Draws two lines from three set points connected in the second set point and returns the inner angle at the second point. \subsection SubFour Draw Four Point Angle Draws two lines that may but must not intersect from four set points. The returned angle is the one depicted in the icon. \subsection SubFive Draw Circle Draws a circle by setting two points, whereas the first set point is the center and the second the radius of the circle. The measured values are the radius and the included area. \subsection SubSix Draw Rectangle Draws a rectangle by setting two points at the opposing edges of the rectangle starting with the upper left edge. The measured values are the circumference and the included area. \subsection SubSeven Draw Polygon Draws a polygon by setting three or more points. The measured values are the circumference and the included area. Add the final point by double left click. Below the buttonline the statistics window is situated, it displays the results of the actual measurements from the selected measurement figures. The content of the statistics window can be copied to the clipboard with the correspondig button for further use in a table calculation programm (e.g. Open Office Calc etc.). \image html Image_processed.JPG The last row contains again a button line to swap from the measurement bundle (activated in the image) to other supported MITK 3M3 bundles. \section QmitkMeasurementUserManualUsage Usage This Section is subdivided into four subsections:
  1. Add an image
  2. Work with measurement figures
  3. Save the image with measurement information
  4. Remove measurement figures or image
Let's start with subsection 1 \subsection One Add an image There are two possible ways to add an image to the programm. One is to grap the image with left mouse click from your prefered file browser and simply drag&drop it to the View Plane field. The other way is to use the \image html OpenButton.png button in the upper left corner of the application. A dialog window appears showing the file tree of the computer. Navigate to the wanted file and select it with the left mouse click. Afterwards just use the dialog's open button. The wanted image appears in the View Plane and in the Data Manager the images name appears as a new tree node. Now the image is loaded it can be adjusted in the usual way ( zoom in/out: right mouse button + moving the mouse up and down, moving the image: press mouse wheel and move the mouse to the wished direction, scroll through the slices( only on 3D images): scroll mouse wheel up and down). \image html Image_Loaded_Screen.JPG After the image is loaded the image's name appears in the Data Manager. By left-clicking on the image name the buttonline becomes activated. \subsection Two Work with measurement figures The measurement module comes with seven measurement figures(see picture below), that can be applied to the images. \image html MeasurementFigureButtonline.jpg The results of the measurement with each of these figures is shown in the statistics window and in the lower right corner of the view plane. \image html Image_processed_Screen.JPG When applying more then one measurement figure to the image the actual measurement figure is depicted in red and the displayed values belong to this measurement figure. All measurement figures become part of the Data Manager as a node of the image tree. \subsection Three Save the image with measurement information After applying the wanted measurement figures the entire scene consisting of the image and the measurement figures can be saved for future use. Therefore just click the right mouse button when over the image item in the Data Manager and choose the item "Save" in the opening item list. Following to that a save dialog appears where the path to the save folder can be set. Afterwards just accept your choice with the save button. \subsection Four Remove measurement figures or image If the single measurement figures or the image is not needed any longer, it can be removed solely or as an entire group. The image can't be removed without simultaneously removing all the dependent measurement figures that belong to the image tree in the Data Manager. To remove just select the wanted items in the data manager list by left-click on it or if several items wanted to be removed left click on all wanted by simultaneously holding the ctrl-button pressed. For more detailed usage of the save/remove functionality refer to the Data Manager User Manual. */ diff --git a/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurementToolbox.dox b/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurementToolbox.dox index d42431ff8e..291d8d2334 100644 --- a/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurementToolbox.dox +++ b/Plugins/org.mitk.gui.qt.measurementtoolbox/documentation/UserManual/QmitkMeasurementToolbox.dox @@ -1,12 +1,12 @@ /** -\page org_measurementtoolbox The Measurement Toolbox Bundle +\page org_mitk_gui_qt_measurementtoolbox The Measurement Toolbox Bundle \section QmitkmeasurementToolbox Manual This bundle contains all views that provide measurement and statistics functionality. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.meshdecimation/documentation/Manual/meshdecimation-manual.dox b/Plugins/org.mitk.gui.qt.meshdecimation/documentation/Manual/meshdecimation-manual.dox index 2403ecd237..43367b70d1 100644 --- a/Plugins/org.mitk.gui.qt.meshdecimation/documentation/Manual/meshdecimation-manual.dox +++ b/Plugins/org.mitk.gui.qt.meshdecimation/documentation/Manual/meshdecimation-manual.dox @@ -1,33 +1,33 @@ /** -\page org_meshdecimation The Mesh Decimation Module +\page org_mitk_views_meshdecimation The Mesh Decimation Module \image html meshdecimation.png "Icon of the Module" Available sections: - \ref meshdecimationOverview - \ref meshdecimationFeatures - \ref meshdecimationUsage \section meshdecimationOverview Overview MeshDecimation is a user friendly tool to decimate a MITK surface. \section meshdecimationFeatures Features The module offers two basic procedures to decimate surfaces: One that reduces a surface with a possible loss of topology (quality), but with a garuanteed reduction rate that is expressed in terms of percent of the original mesh. The other variant preserves the topology and stops decimating when it detects heavy topological changes. \section meshdecimationUsage Usage \image html meshdecimation-ui.png "The user interface of the Mesh Decimation Module" The usage of the module should be straightforward, as shown in the screenshot. To decimate a MITK surface do the following: - Select a surface in the datamanager - Enter a target reduction rate - Select a decimation method (\ref meshdecimationFeatures) - Press the "Decimate" button - Repeat the process until you are satisfied with the decimation - Save the surface to disk */ diff --git a/Plugins/org.mitk.gui.qt.moviemaker/documentation/UserManual/QmitkMovieMakerUserManual.dox b/Plugins/org.mitk.gui.qt.moviemaker/documentation/UserManual/QmitkMovieMakerUserManual.dox index 9117d97603..24068b877c 100644 --- a/Plugins/org.mitk.gui.qt.moviemaker/documentation/UserManual/QmitkMovieMakerUserManual.dox +++ b/Plugins/org.mitk.gui.qt.moviemaker/documentation/UserManual/QmitkMovieMakerUserManual.dox @@ -1,45 +1,45 @@ /** -\page org_moviemaker The Movie Maker Module +\page org_mitk_views_moviemaker The Movie Maker Module \image html icon.png "Icon of the Module" Available sections: - \ref QmitkMovieMakerUserManualOverview - \ref QmitkMovieMakerUserManualFeatures - \ref QmitkMovieMakerUserManualUsage \section QmitkMovieMakerUserManualOverview Overview MovieMaker is a functionality for easily creating fancy movies from scenes displayed in MITK widgets. It is also possible to slide through your data, automatically rotate 3D scenes and take screenshots of widgets. \section QmitkMovieMakerUserManualFeatures Features The Movie Maker allows you to create movies and screenshots from within MITK. It can automatically scroll thorugh timesteps and slices while recording a movie. This way, you can record visualizations like a beating heart or a rotating skull. \section QmitkMovieMakerUserManualUsage Usage \image html QmitkMovieMakerControlArea.png "A view of the command area of QmitkMovieMaker" \subsection QmitkMovieMakerUserManualWindowSelection Window selection With the first two drop down boxes, you can choose which window you want to step through and which window you want to record in. Left clicking inside a window will set both drop down boxes to that window, but you can choose different windows for stepping and recording. The first drop down box defines the window along which slices will be stepped through if stepping is set to spatial (see below). The second denotes the window from which the content will be recorded. \subsection QmitkMovieMakerUserManualRecordingOptions Recording Options The slider can be used to step through the slices manually while not recording. Start and stop control a preview of what a video would look like. The buttons in the bottom part of this section can be used to create movies (windows only) or screenshots. Clicking opens a file %dialog where a name can be selected. After confirmation, a screenshot or movie is created according to the playing options. \subsection QmitkMovieMakerUserManualPlayingOptions Playing Options The first section controls whether the movie steps through slices (if a 2D view is selected), rotate the shown scene (if a 3D view is selected), or step through time steps (if set to temporal and a time resolved dataset is selected). If set to combined, a combination of both above options is used, with their speed relation set via the S/T Relation Spinbox. In the second section the direction of stepping can be set. Options are: Forward, backward and Ping-Pong, which is back-and-forth.The stepping speed can be set via the spinbox(total time in seconds). Although stepping speed is a total time in sec., this can not always be achieved. As a minimal frame rate of 25 fps is assumed to provide smooth movies, a dataset with only 25 slices will always be stepped through in 1 sec or faster. */ diff --git a/Plugins/org.mitk.gui.qt.pointsetinteraction/documentation/UserManual/QmitkPointSetInteractionUserManual.dox b/Plugins/org.mitk.gui.qt.pointsetinteraction/documentation/UserManual/QmitkPointSetInteractionUserManual.dox index adbf30cb2d..56d10b37a8 100644 --- a/Plugins/org.mitk.gui.qt.pointsetinteraction/documentation/UserManual/QmitkPointSetInteractionUserManual.dox +++ b/Plugins/org.mitk.gui.qt.pointsetinteraction/documentation/UserManual/QmitkPointSetInteractionUserManual.dox @@ -1,38 +1,38 @@ /** -\page org_pointsetinteraction The Point Set Interaction Module +\page org_mitk_views_pointsetinteraction The Point Set Interaction Module \image html pointset_interaction.png "Icon of the Module" Available sections: - \ref QmitkPointSetInteractionUserManualOverview - \ref QmitkPointSetInteractionUserManualDetails \section QmitkPointSetInteractionUserManualOverview Overview This functionality allows you to define multiple sets of points, to fill them with points and to save them in so called PointSets. \image html QmitkPointSetInteraction.png "MITK with the QmitkPointSetInteraction functionality" This document will tell you how to use this functionality, but it is assumed that you already know how to navigate through the slices of an image using the four window view. Please read the application manual for more information. \section QmitkPointSetInteractionUserManualDetails Details First of all you have to select a PointSet to use this functionality. Therefore, you have to select the point set in the data manager. If there are currently no point sets in the data tree, you have to first add a new point set to the data tree. This is done by clicking the "Add pointset..." button. \image html AddPointSet.png "The Add pointset... dialog" In the pop-up dialog, you have to specify a name for the new point set. This is also the node for the new data tree item. \image html CurrentPointSetArea.png "The Current pointset area" The "Current pointset" area contains a list of points. Within this area, all points for the current point set node are listed. To set points you have to toggle the "Set Points" button, the leftmost of the four buttons on the bottom of the view. Points can be defined by performing a left mouse button click while holding the "Shift"-key pressed in the four window view. To erase all points from the list press the next button. The user is prompted to confirm the decision. If you want to delete only a single point, left click on it in the list and then press delete on your keyboard. With the third button, a previously saved point set can be loaded and all of its points are shown in the list and the four window view. The user is prompted to select the file to be loaded. The file extension is ".mps". On the right of this button is the save button. With this function the entire point set can be saved to the harddrive. The user is prompted to select a filename. Pointsets are saved in XML fileformat but have to have a ".mps" file extension. You can select points in the render window, if the "Set Points" button is toggled, with a left mouse button click on them. If you keep the mouse button pressed, you can move the points by moving the mouse and then releasing the mouse button. With the delete key you can remove the selected points. */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkDeformableRegistrationUserManual.dox b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkDeformableRegistrationUserManual.dox index 10a3d0cdb5..9e88fa396e 100644 --- a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkDeformableRegistrationUserManual.dox +++ b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkDeformableRegistrationUserManual.dox @@ -1,52 +1,52 @@ /** -\page org_deform_registration The Deformable Image Registration Module +\page org_mitk_views_deformableregistration The Deformable Image Registration Module Available sections: - \ref DeformableRegistrationUserManualOverview - \ref DeformableRegistrationUserManualDetails \section DeformableRegistrationUserManualOverview Overview This module allows you to register 2D as well as 3D images in a deformable manner. Register means to align two images, so that they become as similar as possible. Registration results will directly be applied to the Moving Image. \image html QmitkDeformableRegistration_small.png "MITK with the DeformableRegistration module" This document will tell you how to use this module, but it is assumed that you already know how to navigate through the slices of an image using the multi-widget. \section DeformableRegistrationUserManualDetails Details First of all you have to open the data sets which you want to register and select them in the Data Manager. You have to select exactly 2 images for registration. The image which was selected first will become the fixed image, the other one the moving image. The two selected images will remain for registration until exactly two images were selected in the Data Manager again. While there aren't two images for registration a message is viewed on top of the module saying that registration needs two images. If two images are selected the message disappears and the interaction areas for the fixed and moving data appears. On default only the fixed and moving image are shown in the render windows. If you want to have other images visible you have to set the visibility via the Data Manager. Also if you want to perform a reinit on a specific node or a global reinit for all nodes you have to use the Data Manager. \image html ImageSelectionDeformable.png "The Image area" The upper area is the "Image" area, where the selected images are shown. It is used for changing the colour of the images between grey values and red/green as well as for changing the opacity of the moving image. To do so, just use the "Moving Image Opacity:" slider. In the "Show Images Red/Green" you can switch the color from both datasets. If you check the box, the fixed dataset will be displayed in redvalues and the moving dataset in greenvalues to improve visibility of differences in the datasets. If you uncheck the "Show Images Red/Green" checkbox, both datasets will be displayed in greyvalues. \image html RegistrationDeformable.png "The Registration area for Demons based registration" In the "Registration" area you have the choice between different Demonsbased deformable registration algorithms. There are available: \li Demons Registration \li Symmetric Forces Demons Registration For both methods you have to define the same set of parameters. First you have to decide whether you want to perform a histogram matching. This can be done by selecting "Use Histogram Matching". When it is selected the corresponding parameters are enabled and have to be set. These are the "Number of Histogram Levels", "Number of Match Points" and whether to use a "Threshold at Mean Intensity". For the registration method itself you have to specify the "Number of Iterations" and the "Standard Deviation" within the "Demons Registration" area. If all this is done, you can perform the registration by clicking the "Calculate Transformation" button. Finally, you will be asked where you want the result image and the resulting deformation field to be saved. Therefore you have to select the folder and enter a filename. The results will be added in the DataStorage and can be saved in the Data Manager. */ diff --git a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkPointBasedRegistrationUserManual.dox b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkPointBasedRegistrationUserManual.dox index c10a09068a..ab8841cd4b 100644 --- a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkPointBasedRegistrationUserManual.dox +++ b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkPointBasedRegistrationUserManual.dox @@ -1,106 +1,106 @@ /** -\page org_pointbased_reg The Point Based Registration Module +\page org_mitk_views_pointbasedregistration The Point Based Registration Module \image html pointBasedIcon.png "Icon of the Module" Available sections: - \ref PointBasedRegistrationUserManualOverview - \ref PointBasedRegistrationUserManualDetails \section PointBasedRegistrationUserManualOverview Overview This module allows you to register two datasets in a rigid and deformable manner via corresponding PointSets. Register means to align two datasets, so that they become as similar as possible. Therefore you have to set corresponding points in both datasets, which will be matched. The movement, which has to be performed on the points to align them, will be performed on the moving data as well. The result is shown in the multi-widget. \image html PointBasedRegistration_small.png "MITK with the PointBasedRegistration module" This document will tell you how to use this module, but it is assumed that you already know how to navigate through the slices of a dataset using the multi-widget. \section PointBasedRegistrationUserManualDetails Details First of all you have to open the data sets which you want to register and select them in the Data Manager. You have to select exactly 2 images for registration. The image which was selected first will become the fixed image, the other one the moving image. The two selected images will remain for registration until exactly two images were selected in the Data Manager again. While there aren't two images for registration a message is viewed on top of the module saying that registration needs two images. If two images are selected the message disappears and the interaction areas for the fixed and moving data appears. The upper area is for interaction with the fixed data. Beneath this area is the interaction area for the moving data. On default only the fixed and moving image with their corresponding pointsets are shown in the render windows. If you want to have other images visible you have to set the visibility via the Data Manager. Also if you want to perform a reinit on a specific node or a global reinit for all nodes you have to use the Data Manager. \image html FixedDataPointBased.png "The Fixed Data area" The "Fixed Data" area contains a QmitkPointListWidget. Within this widget, all points for the fixed data are listed. The label above this list shows the number of points that are already set. To set points you have to toggle the "Set Points" button, the leftmost under the QmitkPointListWidget. The views in the QmitkStdMultiWidget were reinitialized to the fixed data. Points can be defined by performing a left click while holding the "Shift"-key pressed in the QmitkStdMultiWidget. You can remove the interactor which listens for left clicks while holding the "Shift"-key pressed by detoggle the "Set Points" button. The next button, "Clear Point Set", is for deleting all specified points from this dataset. The user is prompted to confirm the decision. With the most right button, a previously saved point set can be loaded and all of its points are shown in the QmitkPointListWidget and in the QmitkStdMultiWidget. The user is prompted to select the file to be loaded. The file extension is ".mps". On the left of this button is the save button. With this function all points specified for this dataset and shown in the QmitkPointListWidget are saved to harddisk. The user is prompted to select a filename. Pointsets were saved in XML fileformat but have to have a ".mps" file extension. You can select landmarks in the render window with a left mouse button click on them. If you keep the mouse button pressed you can move the landmark to an other position by moving the mouse and then release the mouse button. With the delete key you can remove the selected landmarks. You can also select landmarks by a double click on a landmark within the QmitkPointListWidget. Using the "Up-Arrow"-button or the "F2" key you can easily move a landmark upwards and bring it further downwards by pressing "F3" or using the "Down-Arrow"-button. Thus the landmark number can be changed. The QmitkStdMultiWidget changes its view to show the position of the landmark. \image html MovingDataPointBased.png "The Moving Data area" The "Moving Data" area contains a QmitkPointListWidget. Within this widget, all points for the moving data are listed. The label above this list shows the number of points that are already set. To set points you have to toggle the "Set Points" button, the leftmost under the QmitkPointListWidget. The views in the QmitkStdMultiWidget were reinitialized to the moving data. With the "Opacity:" slider you can change the opacity of the moving dataset. If the slider is leftmost the moving dataset is totally transparent, whereas if it is rightmost the moving dataset is totally opaque. Points can be defined by performing a left click while holding the "Shift"-key pressed in the QmitkStdMultiWidget. You can remove the interactor which listens for left mousebutton click while holding the "Shift"-key pressed by detoggle the "Set Points" button. The next button, "Clear Point Set", is for deleting all specified points from this dataset. The user is prompted to confirm the decision. With the button on your right hand side, a previously saved point set can be loaded and all of its points are shown in the QmitkPointListWidget and in the QmitkStdMultiWidget. The user is prompted to select the file to be loaded. The file extension is ".mps". On the left of this button is the save button. With this function all points specified for this dataset and shown in the QmitkPointListWidget are saved to harddisk. The user is prompted to select a filename. Pointsets were saved in XML fileformat but have to have a ".mps" file extension. You can select landmarks in the render window with a left click on them. If you keep the mouse button pressed you can move the landmark to an other position by moving the mouse and then release the mouse button. With the delete key you can remove the selected landmarks. You can also select landmarks by a double click on a landmark within the QmitkPointListWidget. Using the "Up-Arrow"-button or the "F2" key you can easily move a landmark upwards and bring it further downwards by pressing "F3" or using the "Down-Arrow"-button. Thus the landmark number can be changed.The QmitkStdMultiWidget changes its view to show the position of the landmark. \image html DisplayOptionsPointBased.png "The Display Options area" In this area you can find the "Show Images Red/Green" checkbox. Here you can switch the color from both datasets. If you check the box, the fixed dataset will be displayed in redvalues and the moving dataset in greenvalues to improve visibility of differences in the datasets. If you uncheck the "Show Images Red/Green" checkbox, both datasets will be displayed in greyvalues. Before you perform your transformation it is useful to see both images again. Therefore detoggle the "Set Points" button for the fixed data as well as for the moving data. \image html RegistrationPointBased.png "The Registration area" The functions concerning the registration are displayed in the "Registration" area. It not only contains the registration method selection and the registration itself but also offers the possibility to save, undo or redo the results. Furthermore a display is implemented, which shows you how good the landmarks correspond. Those features will be explained in following paragraphs. Using the "Method"-selector, you can pick one of those transformations: Rigid, Similarity, Affine and LandmarkWarping. Depending on which one you chose, an additional specifier, "Use ICP" can be set, which leads to the following possibilities for registration: \li Rigid with ICP means only translation and rotation. The order of your landmarks will not be taken into account. E. g. landmark one in the fixed data can be mapped on landmark three in the moving data. You have to set at least one landmark in each dataset to enable the Register button which performs the transformation. \li Similarity with ICP means only translation, scaling and rotation. The order of your landmarks will not be taken into account. E. g. landmark one in the fixed data can be mapped on landmark three in the moving data. You have to set at least one landmark in each dataset to enable the Register button which performs the transformation. \li Affine with ICP means only translation, scaling, rotation and shearing. The order of your landmarks will not be taken into account. E. g. landmark one in the fixed data can be mapped on landmark three in the moving data. You have to set at least one landmark in each dataset to enable the Register button which performs the transformation. \li Rigid means only translation and rotation. The order of your landmarks will be taken into account. E. g. landmark one in the fixed data will be mapped on landmark one in the moving data. You have to set at least one landmark and the same number of landmarks in each dataset to enable the Register button which performs the transformation. \li Similarity means only translation, scaling and rotation. The order of your landmarks will be taken into account. E. g. landmark one in the fixed data will be mapped on landmark one in the moving data. You have to set at least one landmark and the same number of landmarks in each dataset to enable the Register button which performs the transformation. \li Affine means only translation, scaling, rotation and shearing. The order of your landmarks will be taken into account. E. g. landmark one in the fixed data will be mapped on landmark one in the moving data. You have to set at least one landmark and the same number of landmarks in each dataset to enable the Register button which performs the transformation. \li LandmarkWarping means a freeform deformation of the moving image, so that afterwards the landmarks are exactly aligned. The order of your landmarks will be taken into account. E. g. landmark one in the fixed data will be mapped on landmark one in the moving data. You have to set at least one landmark and the same number of landmarks in each dataset to enable the Register button which performs the transformation. The root mean squares difference between the landmarks will be displayed as number, so that you can check how good the landmarks correspond. The "Undo Transformation" button becomes enabled after performing a transformation and allows you to undo it. After doing this, the "Redo Transformation" button is enabled and lets you redo, the just undone transformation(no calculation needed) Saving of the transformed image can be done via the Data Manager. */ diff --git a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkRigidRegistrationUserManual.dox b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkRigidRegistrationUserManual.dox index 77afc8ed3e..089cd17e3a 100644 --- a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkRigidRegistrationUserManual.dox +++ b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/QmitkRigidRegistrationUserManual.dox @@ -1,214 +1,214 @@ /** -\page org_rigid_regis The Rigid Registration Module +\page org_mitk_views_rigidregistration The Rigid Registration Module \image html rigidRegistrationIcon.png "Icon of the Module" Available sections: - \ref QmitkRigidRegistrationUserManualOverview - \ref QmitkRigidRegistrationUserManualIssues - \ref QmitkRigidRegistrationUserManualDetails - \ref QmitkRigidRegistrationUserManualReferences \section QmitkRigidRegistrationUserManualOverview Overview This module allows you to register 2D as well as 3D images in a rigid manner. If the Moving Image is an image with multiple timesteps you can select one timestep for registration. Register means to align two images, so that they become as similar as possible. Therefore you can select from different transforms, metrics and optimizers. Registration results will directly be applied to the Moving Image. Also binary images as image masks can be used to restrict the metric evaluation only to the masked area. \image html RigidRegistration_small.png "MITK with the QmitkRigidRegistration module" This document will tell you how to use this module, but it is assumed that you already know how to navigate through the slices of an image using the multi-widget. \section QmitkRigidRegistrationUserManualIssues Known Issues Depending on your system the registration can fail to allocate memory for calculating the gradient image for registration. In this case you can try to select another optimizer which is not based on a gradient image and uncheck the checkbox for "Compute Gradient". \section QmitkRigidRegistrationUserManualDetails Details First of all you have to open the data sets which you want to register and select them in the Data Manager. You have to select exactly 2 images for registration. The image which was selected first will become the fixed image, the other one the moving image. The two selected images will remain for registration until exactly two images were selected in the Data Manager again. \image html ImageArea.png "The Image area" While there aren't two images for registration a message is viewed on top of the module saying that registration needs two images. If two images are selected the message disappears and the interaction areas for the fixed and moving data appears. If both selected images have a binary image as childnode a selection box appears which allows, when checked, to use the binary images as image mask to restrict the registration on this certain area. If an image has more than one binary image as child, the upper one from the DataManager list is used. If the Moving Image is a dynamic images with several timesteps a slider appears to select a specific timestep for registration. On default only the fixed and moving image are shown in the render windows. If you want to have other images visible you have to set the visibility via the Data Manager. Also if you want to perform a reinit on a specific node or a global reinit for all nodes you have to use the Data Manager. The colour of the images can be changed between grey values and red/green and the opacity of the moving image can be changed. With the "Moving Image Opacity:" slider you can change the opacity of the moving dataset. In the "Show Images Red/Green" you can switch the color from both datasets. If you check the box, the fixed dataset will be displayed in red-values and the moving dataset in green-values to improve visibility of differences in the datasets. If you uncheck the "Show Images Red/Green" checkbox, both datasets will be displayed in grey-values. \image html RegistrationArea.png "The Registration area" In the "Register" area you can start the registration by clicking the "Calculate Transform" button. The optimizer value for every iteration step is diplayed as LCD number next to the "Optimizer Value:" label. Many of the registration methods can be canceled during their iteration steps by clicking the "Stop Optimization" button. During the calculation, a progress bar indicates the progress of the registration process. The render widgets are updated for every single iteration step, so that the user has the chance to supervise how good the registration process works with the selected methods and parameters. If the registration process does not lead to a sufficient result, it is possible to undo the transformation and restart the registration process with some changes in parameters. The differences in transformation due to the changed parameters can be seen in every iteration step and help the user understand the parameters. Also the optimizer value is updated for every single iteration step and shown in the GUI. The optimizer value is an indicator for the misalignment between the two images. The real time visualization of the registration as well as the optimizer value provides the user with information to trace the improvement through the optimization process. The "Undo Transformation" button becomes enabled when you have performed an transformation and you can undo the performed transformations. The "Redo Transformation" button becomes enabled when you have performed an undo to redo the transformation without to recalculate it. \image html ManualRegistrationArea.png "The Manual Registration area" In the "Manual Registration" area, shown by checking the checkbox Manual Registration, you can manually allign the images by moving sliders for translation and scaling in x-, y- and z-axis as well as for rotation around the x-, y- and z-Axis. Additionally you can automatically allign the image centers with the button "Automatic Allign Image Centers". \image html Tab2.png "The Advanced Mode tab" In the "Advanced Mode" tab you can choose a transform, a metric, an optimizer and an interpolator and you have to set the corresponding parameters to specify the registration method you want to perform. With the topmost button you can also load testpresets. These presets contain all parametersets which were saved using the "Save as Testpreset" button. The "Save as Preset" button makes the preset available from the "Automatic Registration" tab. This button should be used when a preset is not intended for finding good parameters anymore but can be used as standard preset. To show the current transform and its parameters for the registration process, the Transform checkbox has to be checked. Currently, the following transforms are implemented (for detailed information see [1] and [2]): \li Translation: Transformation by a simple translation for every dimension. \li Scale: Transformation by a certain scale factor for each dimension. \li ScaleLogarithmic: Transformation by a certain scale factor for each dimension. The parameter factors are passed as logarithms. \li Affine: Represents an affine transform composed of rotation, scaling, shearing and translation. \li FixedCenterOfRotationAffine: Represents an affine transform composed of rotation around a user provided center, scaling, shearing and translation. \li Rigid3D: Represents a 3D rotation followed by a 3D translation. \li Euler3D: Represents a rigid rotation in 3D space. That is, a rotation followed by a 3D translation. \li CenteredEuler3D: Represents a rigid rotation in 3D space around a user provided center. That is, a rotation followed by a 3D translation. \li QuaternionRigid: Represents a 3D rotation and a 3D translation. The rotation is specified as a quaternion. \li Versor: Represents a 3D rotation. The rotation is specified by a versor or unit quaternion. \li VersorRigid3D: Represents a 3D rotation and a 3D translation. The rotation is specified by a versor or unit quaternion. \li ScaleSkewVersor3D: Represents a 3D translation, scaling, shearing and rotation. The rotation is specified by a versor or unit quaternion. \li Similarity3D: Represents a 3D rotation, a 3D translation and homogeneous scaling. \li Rigid2D: Represents a 2D rotation followed by a 2D translation. \li CenteredRigid2D: Represents a 2D rotation around a user provided center followed by a 2D translation. \li Euler2D: Represents a 2D rotation and a 2D translation. \li Similarity2D: Represents a 2D rotation, homogeneous scaling and a 2D translation. \li CenteredSimilarity2D: Represents a 2D rotation around a user provided center, homogeneous scaling and a 2D translation. The desired transform can be chosen from a combo box. All parameters defining the selected transform have to be specified within the line edits and checkboxes underneath the transform combo box. To show the current metric and its parameters for the registration process, the Metric checkbox has to be checked. Currently, the following metrics are implemented (for detailed information see [1] and [2]): \li MeanSquares: Computes the mean squared pixel-wise difference in intensity between image A and B. \li NormalizedCorrelation: Computes pixel-wise cross correlation and normalizes it by the square root of the autocorrelation of the images. \li GradientDifference: Evaluates the difference in the derivatives of the moving and fixed images. \li KullbackLeiblerCompareHistogram[3]: Measures the relative entropy between two discrete probability distributions. \li CorrelationCoefficientHistogram: Computes the cross correlation coefficient between the intensities. \li MeanSquaresHistogram: The joint histogram of the fixed and the mapped moving image is built first. Then the mean squared pixel-wise difference in intensity between image A and B is calculated. \li MutualInformationHistogram: Computes the mutual information between image A and image B. \li NormalizedMutualInformationHistogram: Computes the mutual information between image A and image B. \li MattesMutualInformation[4, 5]: The method of Mattes et al. is used to compute the mutual information between two images to be registered. \li MeanReciprocalSquareDifference: Computes pixel-wise differences and adds them after passing them through a bell-shaped function 1 / (1+x^2). \li MutualInformation[6]: Computes the mutual information between image A and image B. \li MatchCardinality: Computes cardinality of the set of pixels that match exactly between the moving and fixed images. \li KappaStatistic[7]: Computes spatial intersection of two binary images. The desired metric can be chosen from a combo box. All parameters defining the selected metric have to be specified within the line edits and checkboxes underneath the metric combo box. To show the current optimizer and its parameters for the registration process, the Optimizer checkbox has to be checked. Currently, the following optimizers are implemented (for detailed information see [1] and [2]): \li Exhaustive: Fully samples a grid on the parametric space. \li GradientDescent: A simple gradient descent optimizer. \li QuaternionRigidTransformGradientDescent: Variant of a gradient descent optimizer. \li LBFGSB[8, 9]: Limited memory Broyden Fletcher Goldfarb Shannon minimization with simple bounds. \li OnePlusOneEvolutionary[10]: 1+1 evolutionary strategy. \li Powell: Implements Powell optimization using Brent line search. \li FRPR: Fletch-Reeves & Polak-Ribiere optimization using dBrent line search. \li RegularStepGradientDescent: Variant of a gradient descent optimizer. \li VersorTransform: Variant of a gradient descent optimizer. \li Amoeba: Implementation of the Nelder-Meade downhill simplex algorithm. \li ConjugateGradient: Used to solve unconstrained optimization problems. \li LBFGS: Limited memory Broyden Fletcher Goldfarb Shannon minimization. \li SPSA[11]: Based on simultaneous perturbation. \li VersorRigid3DTransform: Variant of a gradient descent optimizer for the VersorRigid3DTransform parameter space. The desired optimizer can be chosen from a combo box. All parameters defining the selected optimizer have to be specified within the line edits and checkboxes underneath the optimizer combo box. To show the current interpolator for the registration process, just check the Interpolator checkbox. Currently, the following interpolators are implemented (for detailed information see [1] and [2]): \li Linear: Intensity varies linearly between grid positions. \li NearestNeighbor: Uses the intensity of the nearest grid position. You can show and hide the parameters for the selection by checking or unchecking the corresponding area. You can save the current sets of parameters with the "Save as Testpreset" or "Save as Preset" buttons. \section QmitkRigidRegistrationUserManualReferences References: 1. L. Ibanez, W. Schroeder and K. Ng, The ITK Software Guide, Kitware Inc, New York, 2005. 2. http://www.itk.org/Doxygen/ 3. Albert C.S. Chung, William M. Wells III, Alexander Norbash, and W. Eric L. Grimson, Multi-modal Image Registration by Minimising Kullback-Leibler Distance, In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002, LNCS 2489, pp. 525 - 532. 4. D. Mattes, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank, "Nonrigid multimodality image registration", Medical Imaging 2001: Image Processing, 2001, pp. 1609-1620. 5. D. Mattes, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank, "PET-CT Image Registration in the Chest Using Free-form Deformations", IEEE Transactions in Medical Imaging. Vol.22, No.1, January 2003, pp.120-128. 6. Viola, P. and Wells III, W. (1997). "Alignment by Maximization of Mutual Information" International Journal of Computer Vision, 24(2):137-154. 7. AP Zijdenbos, BM Dawant, RA Margolin , AC Palmer, Morphometric analysis of white matter lesions in MR images: Method and validation, IEEE Transactions on Medical Imaging, 13(4):716-724, Dec. 1994. 8. R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing , 16, 5, pp. 1190-1208. 9. C. Zhu, R. H. Byrd and J. Nocedal. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (1997), ACM Transactions on Mathematical Software, Vol 23, Num. 4, pp. 550 - 560. 10. Martin Styner, G. Gerig, Christian Brechbuehler, Gabor Szekely, "Parametric estimate of intensity inhomogeneities applied to MRI", IEEE TRANSACTIONS ON MEDICAL IMAGING; 19(3), pp. 153-165, 2000. 11. Spall, J.C. (1998), "An Overview of the Simultaneous Perturbation Method for Efficient Optimization," Johns Hopkins APL Technical Digest, vol. 19, pp. 482-492. */ diff --git a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/RegistrationModuleOverview.dox b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/RegistrationModuleOverview.dox index fdea9e2a31..1f43c7a195 100644 --- a/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/RegistrationModuleOverview.dox +++ b/Plugins/org.mitk.gui.qt.registration/documentation/UserManual/RegistrationModuleOverview.dox @@ -1,14 +1,14 @@ /** -\page RegistrationModuleOverviewPage The Registration Modules +\page org_mitk_gui_qt_registration The Registration Modules \section RegistrationModuleOverviewPageOverview Overview MITK provides several modules for the registration of images. \section RegistrationModuleOverviewPageList List of Modules - \li \subpage org_deform_registration - \li \subpage org_pointbased_reg - \li \subpage org_rigid_regis + \li \subpage org_mitk_views_deformableregistration + \li \subpage org_mitk_views_pointbasedregistration + \li \subpage org_mitk_views_rigidregistration */ \ No newline at end of file diff --git a/Plugins/org.mitk.gui.qt.segmentation/documentation/UserManual/org_mitk_gui_qt_segmentation.dox b/Plugins/org.mitk.gui.qt.segmentation/documentation/UserManual/org_mitk_gui_qt_segmentation.dox index d4b193a455..463e51d867 100644 --- a/Plugins/org.mitk.gui.qt.segmentation/documentation/UserManual/org_mitk_gui_qt_segmentation.dox +++ b/Plugins/org.mitk.gui.qt.segmentation/documentation/UserManual/org_mitk_gui_qt_segmentation.dox @@ -1,292 +1,292 @@ /** -\page org_segment The Segmentation Module +\page org_mitk_views_segmentation The Segmentation Module \image html segmentation.png "Icon of the Module" Some of the features described below are not available in the open-source part of the MITK-3M3-Application. Available sections: - \ref org_mitk_gui_qt_segmentationUserManualOverview - \ref org_mitk_gui_qt_segmentationUserManualTechnical - \ref org_mitk_gui_qt_segmentationUserManualImageSelection - \ref org_mitk_gui_qt_segmentationUserManualManualKringeling - \ref org_mitk_gui_qt_segmentationUserManualManualKringeling1 - \ref org_mitk_gui_qt_segmentationUserManualManualKringeling2 - \ref org_mitk_gui_qt_segmentationUserManualManualKringeling3 - \ref org_mitk_gui_qt_segmentationUserManualManualKringeling4 - \ref org_mitk_gui_qt_segmentationUserManualManualKringeling5 - \ref org_mitk_gui_qt_segmentationUserManualOrganSegmentation - \ref org_mitk_gui_qt_segmentationUserManualOrganSegmentation1 - \ref org_mitk_gui_qt_segmentationUserManualOrganSegmentation2 - \ref org_mitk_gui_qt_segmentationUserManualOrganSegmentation99 - \ref org_mitk_gui_qt_segmentationUserManualLesionSegmentation - \ref org_mitk_gui_qt_segmentationUserManualPostprocessing - \ref org_mitk_gui_qt_segmentationUserManualSurfaceMasking - \ref org_mitk_gui_qt_segmentationUserManualTechnicalDetail \section org_mitk_gui_qt_segmentationUserManualOverview Overview The Segmentation perspective allows you to create segmentations of anatomical and pathological structures in medical images of the human body. The perspective groups a number of tools which can be used for: \image html org_mitk_gui_qt_segmentationIMGapplication.png Segmentation perspective consisting of the Data Manager view and the Segmentation view If you wonder what segmentations are good for, we shortly revisit the concept of a segmentation here. A CT or MR image is made up of volume of physical measurements (volume elements are called voxels). In CT images, for example, the gray value of each voxel corresponds to the mass absorbtion coefficient for X-rays in this voxel, which is similar in many %parts of the human body. The gray value does not contain any further information, so the computer does not know whether a given voxel is part of the body or the background, nor can it tell a brain from a liver. However, the distinction between a foreground and a background structure is required when: Creating this distinction between foreground and background is called segmentation. The Segmentation perspective of the MITK Workbench uses a voxel based approach to segmentation, i.e. each voxel of an image must be completely assigned to either foreground or background. This is in contrast to some other applications which might use an approach based on contours, where the border of a structure might cut a voxel into two %parts. The remainder of this document will summarize the features of the Segmentation perspective and how they are used. \section org_mitk_gui_qt_segmentationUserManualTechnical Technical Issues The Segmentation perspective makes a number of assumptions. To know what this module can be used for, it will help you to know that: \section org_mitk_gui_qt_segmentationUserManualImageSelection Image Selection The Segmentation perspective makes use of the Data Manager view to give you an overview of all images and segmentations. \image html org_mitk_gui_qt_segmentationIMGselection.png Data Manager is used for selecting the current segmentation. The reference image is selected in the drop down box of the control area. To select the reference image (e.g. the original CT/MR image) use the drop down box in the control area of the Segmentation view. The segmentation image selected in the Data Manager is displayed below the drop down box. If no segmentation image exists or none is selected create a new segmentation image by using the "New segmentation" button. Some items of the graphical user interface might be disabled when no image is selected. In any case, the application will give you hints if a selection is needed. \section org_mitk_gui_qt_segmentationUserManualManualKringeling Manual Contouring With manual contouring you define which voxels are part of the segmentation and which are not. This allows you to create segmentations of any structeres that you may find in an image, even if they are not part of the human body. You might also use manual contouring to correct segmentations that result from sub-optimal automatic methods. The drawback of manual contouring is that you might need to define contours on many 2D slices. However, this is moderated by the interpolation feature, which will make suggestions for a segmentation. \subsection org_mitk_gui_qt_segmentationUserManualManualKringeling1 Creating New Segmentations Unless you want to edit existing segmentations, you have to create a new, empty segmentation before you can edit it. To do so, click the "New manual segmentation" button. Input fields will appear where you can choose a name for the new segmentation and a color for its display. Click the checkmark button to confirm or the X button to cancel the new segmentation. Notice that the input field suggests names once you %start typing and that it also suggests colors for known organ names. If you use names that are not yet known to the application, it will automatically remember these names and consider them the next time you create a new segmentation. Once you created a new segmentation, you can notice a new item with the "binary mask" icon in the Data Manager tree view. This item is automatically selected for you, allowing you to %start editing the new segmentation right away. \subsection org_mitk_gui_qt_segmentationUserManualManualKringeling2 Selecting Segmentations for Editing As you might want to have segmentations of multiple structures in a single patient image, the application needs to know which of them to use for editing. You select a segmenation by clicking it in the tree view of Data Manager. Note that segmentations are usually displayed as sub-items of "their" patient image. In the rare case, where you need to edit a segmentation that is not displayed as a a sub-item, you can click both the original image AND the segmentation while holding down CTRL or for Mac OS X the CMD on the keyboard. When a selection is made, the Segmentation View will hide all but the selected segmentation and the corresponding original image. When there are multiple segmentations, the unselected ones will remain in the Data Manager, you can make them visible at any time by selecting them. \subsection org_mitk_gui_qt_segmentationUserManualManualKringeling3 Selecting Editing Tools If you are familiar with the MITK Workbench, you know that clicking and moving the mouse in any of the 2D render windows will move around the crosshair that defines what part of the image is displayed. This behavior is disabled while any of the manual segmentation tools are active -- otherwise you might have a hard time concentrating on the contour you are drawing. To %start using one of the editing tools, click its button the the displayed toolbox. The selected editing tool will be active and its corresponding button will stay pressed until you click the button again. Selecting a different tool also deactivates the previous one. If you have to delineate a lot of images, you should try using shortcuts to switch tools. Just hit the first letter of each tool to activate it (A for Add, S for Subtract, etc.). \subsection org_mitk_gui_qt_segmentationUserManualManualKringeling4 Using Editing Tools All of the editing tools work by the same principle: you use the mouse (left button) to click anywhere in a 2D window (any of the orientations axial, sagittal, or frontal), move the mouse while holding the mouse button and release to finish the editing action. Multi-step undo and redo is fully supported by all editing tools. Use the application-wide undo button in the toolbar to revert erroneous %actions. \image html org_mitk_gui_qt_segmentationIMGiconAddSubtract.png Add and Subtract Tools Use the left mouse button to draw a closed contour. When releasing the mouse button, the contour will be added (Add tool) to or removed from (Subtract tool) the current segmentation. Hold down the CTRL / CMD key to invert the operation (this will switch tools temporarily to allow for quick corrections). \image html org_mitk_gui_qt_segmentationIMGiconPaintWipe.png Paint and Wipe Tools Use the slider below the toolbox to change the radius of these round paintbrush tools. Move the mouse in any 2D window and press the left button to draw or erase pixels. As the Add/Subtract tools, holding CTRL / CMD while drawing will invert the current tool's behavior. \image html org_mitk_gui_qt_segmentationIMGiconRegionGrowing.png Region Growing Tool Click at one point in a 2D slice widget to add an image region to the segmentation with the region growing tool. Moving up the cursor while holding the left mouse button widens the range for the included grey values; moving it down narrows it. When working on an image with a high range of grey values, the selection range can be influenced more strongly by moving the cursor at higher velocity. Region Growing selects all pixels around the mouse cursor that have a similar gray value as the pixel below the mouse cursor. This enables you to quickly create segmentations of structures that have a good contrast to surrounding tissue, e.g. the lungs. The tool will select more or less pixels (corresponding to a changing gray value interval width) when you move the mouse up or down while holding down the left mouse button. A common issue with region growing is the so called "leakage" which happens when the structure of interest is connected to other pixels, of similar gray values, through a narrow "bridge" at the border of the structure. The Region Growing tool comes with a "leakage detection/removal" feature. If leakage happens, you can left-click into the leakage region and the tool will try to automatically remove this region (see illustration below). \image html org_mitk_gui_qt_segmentationIMGleakage.png Leakage correction feature of the Region Growing tool
\image html org_mitk_gui_qt_segmentationIMGiconCorrection.png Correction Tool You do not have to draw a closed contour to use the Correction tool and do not need to switch between the Add and Substract tool to perform small corrective changes. The following figure shows the usage of this tool: \image html org_mitk_gui_qt_segmentationIMGcorrectionActions.png %Actions of the Correction tool illustrated.
\image html org_mitk_gui_qt_segmentationIMGiconFill.png Fill Tool Left-click inside a segmentation with holes to completely fill all holes. \image html org_mitk_gui_qt_segmentationIMGiconErase.png Erase Tool This tool removes a connected part of pixels that form a segmentation. You may use it to remove so called islands (see picture) or to clear a whole slice at once (hold CTRL while clicking). \subsection org_mitk_gui_qt_segmentationUserManualManualKringeling5 Interpolation Creating segmentations for modern CT volumes is very time-consuming, because structures of interest can easily cover a range of 50 or more slices. The Manual Segmentation View offers two helpful features for these cases:
The 3D interpolation is activated by default when using the manual segmentation tools. That means if you start contouring, from the second contour onwards, the surface of the segmented area will be interpolated based on the given contour information. The interpolation works with all available manual tools. Please note that this is currently a pure mathematical interpolation, i.e. image intensity information is not taken into account. With each further contour the interpolation result will be improved, but the more contours you provide the longer the recalculation will take. To achieve an optimal interpolation result and in this way a most accurate segmentation you should try to describe the surface with sparse contours by segmenting in arbitrary oriented planes. The 3D interpolation is not meant to be used for parallel slice-wise segmentation. \image html org_mitk_gui_qt_segmentation3DInterpolationWrongRight.png 3D Interpolation HowTo You can accept the interpolation result by clicking the "Accept" - button below the tool buttons. In this case the 3D interpolation will be deactivated automatically so that the result can be postprocessed without any interpolation running in background. During recalculation the interpolated surface is blinking yellow/white. When the interpolation has finished the surface is shown yellow with a small opacity. Additional to the surface, black contours are shown in the 3D render window. They mark the positions of all the drawn contours which were used for the interpolation. You can navigate between the drawn contours by clicking on the „Position“ - Nodes in the datamanager which are located below the selected segmentation. If you don't want to see these nodes just unckeck the „Show Position Nodes“ Checkbox and these nodes will be hidden. If you want to delete a drawn contour we recommend to use the Erase-Tool since Redo/Undo is not yet working for 3D interpolation.
The 2D Interpolation creates suggestions for a segmentation whenever you have a slice that Interpolated suggestions are displayed in a different way than manual segmentations are, until you "accept" them as part of the segmentation. To accept single slices, click the "Accept" button below the toolbox. If you have segmented a whole organ in every-x-slice, you may also review the interpolations and then accept all of them at once by clicking "... all slices". \section org_mitk_gui_qt_segmentationUserManualOrganSegmentation Organ Segmentation \note This feature is only available in our 3M3 Demo Application (http://www.mint-medical.de/productssolutions/mitk3m3/mitk3m3/#downloads) but not in the open source part of MITK The manual contouring described above is a fallback option that will work for any kind of images and structures of interest. However, manual contouring is very time-consuming and tedious. This is why a major part of image analysis research is working towards automatic segmentation methods. The Segmentation View comprises a number of easy-to-use tools for segmentation of CT images (Liver) and MR image (left ventricle and wall, left and right lung). \subsection org_mitk_gui_qt_segmentationUserManualOrganSegmentation1 Liver on CT Images On CT image volumes, preferrably with a contrast agent in the portal venous phase, the Liver tool will fully automatically analyze and segment the image. All you have to do is to load and select the image, then click the "Liver" button. During the process, which takes a minute or two, you will get visual progress feedback by means of a contour that moves closer and closer to the real liver boundaries. \subsection org_mitk_gui_qt_segmentationUserManualOrganSegmentation2 Heart, Lung, and Hippocampus on MRI While liver segmentation is performed fully automatic, the following tools for segmentation of the heart, the lungs, and the hippocampus need a minimum amount of guidance. Click one of the buttons on the "Organ segmentation" page to add an average %model of the respective organ to the image. This %model can be dragged to the right position by using the left mouse button while holding down the CTRL key. You can also use CTRL + middle mouse button to rotate or CTRL + right mouse button to scale the %model. Before starting the automatic segmentation process by clicking the "Start segmentation" button, try placing the %model closely to the organ in the MR image (in most cases, you do not need to rotate or scale the %model). During the segmentation process, a green contour that moves closer and closer to the real liver boundaries will provide you with visual feedback of the segmentation progress. The algorithms used for segmentation of the heart and lung are method which need training by a number of example images. They will not work well with other kind of images, so here is a list of the image types that were used for training: \subsection org_mitk_gui_qt_segmentationUserManualOrganSegmentation99 Other Organs As mentioned in the Heart/Lung section, most of the underlying methods are based on "training". The basic algorithm is versatile and can be applied on all kinds of segmentation problems where the structure of interest is topologically like a sphere (and not like a torus etc.). If you are interested in other organs than those offered by the current version of the Segmentation view, please contact our research team. \section org_mitk_gui_qt_segmentationUserManualLesionSegmentation Lesion Segmentation \note This feature is only available in our 3M3 Demo Application (http://www.mint-medical.de/productssolutions/mitk3m3/mitk3m3/#downloads) but not in the open source part of MITK Lesion segmentation is a little different from organ segmentation. Since lesions are not part of the healthy body, they sometimes have a diffused border, and are often found in varying places all over the body. The tools in this section offer efficient ways to create 3D segmentations of such lesions. The Segmentation View currently offers supoprt for enlarged lymph nodes. To segment an enlarged lymph node, find a more or less central slice of it, activate the "Lymph Node" tool and draw a rough contour on the inside of the lymph node. When releaseing the mouse button, a segmentation algorithm is started in a background task. The result will become visible after a couple of seconds, but you do not have to wait for it. If you need to segment several lymph nodes, you can continue to inspect the image right after closing the drawn contour. If the lymph node segmentation is not to your content, you can select the "Lymph Node Correction" tool and drag %parts of the lymph node surface towards the right position (works in 3D, not slice-by-slice). This kind of correction helps in many cases. If nothing else helps, you can still use the pure manual tools as a fallback. \section org_mitk_gui_qt_segmentationUserManualPostprocessing Things you can do with segmentations As mentioned in the introduction, segmentations are never an end in themselves. Consequently, the Segmentation view adds a couple of "post-processing" %actions to the Data Manager. These %actions are accessible through the context-menu of segmentations in Data Manager's list view \image html org_mitk_gui_qt_segmentationIMGDataManagerContextMenu.png Context menu items for segmentations. \section org_mitk_gui_qt_segmentationUserManualSurfaceMasking Surface Masking You can use the surface masking tool to create binary images from a surface which is used used as a mask on an image. This task is demonstrated below: \image html segmentationFromSurfaceBefore.png Load an image and a surface. Select the image and the surface in the corresponding drop-down boxes (both are selected automatically if there is just one image and one surface) \image html segmentationFromSurfaceAfter.png Create segmentation from surface After clicking "Create segmentation from surface" the newly created binary image is inserted in the DataManager and can be used for further processing \section org_mitk_gui_qt_segmentationUserManualTechnicalDetail Technical Information for Developers For technical specifications see \subpage QmitkSegmentationTechnicalPage and for information on the extensions of the tools system \subpage toolextensions . */ diff --git a/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualizationUserManual.dox b/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualizationUserManual.dox index c69d9027ee..2972552f37 100644 --- a/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualizationUserManual.dox +++ b/Plugins/org.mitk.gui.qt.volumevisualization/documentation/UserManual/QmitkVolumeVisualizationUserManual.dox @@ -1,150 +1,150 @@ /** -\page org_volvis The Volume Visualization Module +\page org_mitk_views_volumevisualization The Volume Visualization Module \image html icon.png "Icon of the Module" Available sections: \section QVV_Overview Overview The Volume Visualization Module is a basic tool for visualizing three dimensional medical images. MITK provides generic transfer function presets for medical CT data. These functions, that map the gray-value to color and opacity, can be interactively edited. Additionally, there are controls to quickly generate common used transfer function shapes like the threshold and bell curve to help identify a range of grey-values. \image html vroverview.png "" \section QVV_EnableVRPage Enable Volume Rendering \subsection QVV_LoadingImage Loading an image into the application Load an image into the application by Volume Visualization imposes following restrictions on images: \subsection QVV_EnableVR Enable Volumerendering \image html checkboxen.png "" Select an image in datamanager and click on the checkbox left of "Volumerendering". Please be patient, while the image is prepared for rendering, which can take up to a half minute. \subsection QVV_LODGPU The LOD & GPU checkboxes Volume Rendering requires a lot of computing resources including processor, memory and graphics card. To run volume rendering on smaller platforms, enable the LOD checkbox (level-of-detail rendering). Level-of-detail first renders a lower quality preview to increase interactivity. If the user stops to interact a normal quality rendering is issued. The GPU checkbox tries to use computing resources on the graphics card to accelerate volume rendering. It requires a powerful graphics card and OpenGL hardware support for shaders, but achieves much higher frame rates than software-rendering. \section QVV_PresetPage Applying premade presets \subsection QVV_Preset Internal presets There are some internal presets given, that can be used with normal CT data (given in Houndsfield units). A large set of medical data has been tested with that presets, but it may not suit on some special cases. Click on the "Preset" tab for using internal or custom presets. \image html mitkInternalPresets.png "" \subsection QVV_CustomPreset Saving and loading custom presets After creating or editing a transferfunction (see \ref QVV_Editing or \ref QVV_ThresholdBell), the custom transferfunction can be stored and later retrieved on the filesystem. Click "Save" (respectively "Load") button to save (load) the threshold-, color- and gradient function combined in a single .xml file. \section QVV_ThresholdBell Interactively create transferfunctions Beside the possibility to directly edit the transferfunctions (\ref QVV_Editing), a one-click generation of two commonly known shapes is given. Both generators have two parameters, that can be modified by first clicking on the cross and then moving the mouse up/down and left/right. The first parameter "center" (controlled by horizontal movement of the mouse) specifies the gravalue where the center of the shape will be located. The second parameter "width" (controlled by vertical movement of the mouse) specifies the width (or steepness) of the shape. \subsection Threshold Click on the "Threshold" tab to active the threshold function generator. \image html threshold.png "" A threshold shape begins with zero and raises to one across the "center" parameter. Lower widths results in steeper threshold functions. \subsection Bell Click on the "Bell" tab to active the threshold function generator. \image html bell.png "" A threshold shape begins with zero and raises to one at the "center" parameter and the lowers agains to zero. The "width" parameter correspondens to the width of the bell. \section QVV_Editing Customize transferfunctions in detail \subsection QVV_Navigate Choosing grayvalue interval to edit \image html slider.png "" To navigate across the grayvalue range or to zoom in some ranges use the "range"-slider. All three function editors have in common following: There are three transferfunctions to customize: \subsection QVV_GO Grayvalue -> Opacity \image html opacity.png "grayvalues will be mapped to opacity." An opacity of 0 means total transparent, an opacity of 1 means total opaque. \subsection QVV_GC Grayvalue -> Color \image html color.png "grayvalues will be mapped to color." The color transferfunction editor also allows by double-clicking a point to change its color. \subsection QVV_GGO Grayvalue and Gradient -> Opacity \image html gradient.png "" Here the influence of the gradient is controllable at specific grayvalues. */