diff --git a/Documentation/Doxygen/2-UserManual/MITKPluginGeneralManualsList.dox b/Documentation/Doxygen/2-UserManual/MITKPluginGeneralManualsList.dox index 3d38945f38..16f2403983 100644 --- a/Documentation/Doxygen/2-UserManual/MITKPluginGeneralManualsList.dox +++ b/Documentation/Doxygen/2-UserManual/MITKPluginGeneralManualsList.dox @@ -1,32 +1,32 @@ /** \page PluginListGeneralPage List of General Purpose Plugins \li \subpage org_mitk_views_basicimageprocessing \li \subpage org_mitk_views_datamanager \li \subpage org_mitk_views_properties \li \subpage org_mitk_gui_qt_dicom \li \subpage org_mitk_gui_qt_dicominspector \li \subpage org_mitk_gui_qt_imagecropper \li \subpage org_mitk_views_imagenavigator \li \subpage org_blueberry_ui_qt_log \li \subpage org_mitk_gui_qt_matchpoint_algorithm_batch - \li \subpage org_mitk_gui_qt_matchpoint_algorithm_browser - \li \subpage org_mitk_gui_qt_matchpoint_algorithm_control - \li \subpage org_mitk_gui_qt_matchpoint_evaluator - \li \subpage org_mitk_gui_qt_matchpoint_framereg - \li \subpage org_mitk_gui_qt_matchpoint_manipulator - \li \subpage org_mitk_gui_qt_matchpoint_mapper - \li \subpage org_mitk_gui_qt_matchpoint_visualizer + \li \subpage org_mitk_views_qt_matchpoint_algorithm_browser + \li \subpage org_mitk_views_qt_matchpoint_algorithm_control + \li \subpage org_mitk_views_qt_matchpoint_evaluator + \li \subpage org_mitk_views_qt_matchpoint_framereg + \li \subpage org_mitk_views_qt_matchpoint_manipulator + \li \subpage org_mitk_views_matchpoint_mapper + \li \subpage org_mitk_views_qt_matchpoint_visualizer \li \subpage org_mitk_gui_qt_measurementtoolbox \li \subpage org_mitk_views_moviemaker \li \subpage org_mitk_views_multilabelsegmentation \li \subpage org_mitk_views_pointsetinteraction \li \subpage org_mitk_gui_qt_python \li \subpage org_mitk_gui_qt_remeshing \li \subpage org_mitk_views_screenshotmaker \li \subpage org_mitk_views_segmentation \li \subpage org_mitk_gui_qt_flow_segmentation \li \subpage org_mitk_gui_qt_viewnavigator \li \subpage org_mitk_views_volumevisualization */ diff --git a/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox b/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox index 26d569a356..41b0c0d201 100644 --- a/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox +++ b/Documentation/Doxygen/2-UserManual/MITKPluginManualsList.dox @@ -1,84 +1,84 @@ /** \page PluginListPage MITK Plugin Manuals The plugins 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 plugins as needed. \subpage PluginListGeneralPage \subpage PluginListSpecificPage */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox b/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox index e064239b1c..c3f3652037 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox +++ b/Plugins/org.mitk.gui.qt.matchpoint.mapper/documentation/UserManual/Manual.dox @@ -1,78 +1,79 @@ /** \page org_mitk_views_matchpoint_mapper The MatchPoint Image Mapper View \imageMacro{map_mapper_icon_doc.svg, "Icon of the MatchPoint Image Mapper",3} \tableofcontents \section MAP_MAPPER_Introduction Introduction This view offers the possibility to map any loaded image or point set using a user selected registration object. When mapping images the user can control the field of view (image geometry) the image should be mapped into, as well as the interpolation strategy and padding values that should be used. It is one of several MatchPoint registration plugins. Typical usage scenarios\n -\li You have registered image I1 onto image I2. (Most obious) you want to map I1 onto I2 with the registration, e.g. to make a joint statistical analysis. -\li You have registered image I1 onto image I2. Now you want to map I3 (e.g. an other MRI sequence of the same session) also onto I2 with the same registration. -\li You have registered image I1 onto image I2. Now you want to map a segmentation created on I1 also onto I2 with the same registration. -\li You have registered image I1 onto image I2. Now you want to map a point set of image I1 also onto I2 with the same registration. +You have registered image I1 onto image I2. Now you want to +\li (Most obvious) map I1 onto I2 with the registration, e.g. to make a joint statistical analysis. +\li map image I3 (e.g. an other MRI sequence of the same session) also onto I2 with the same registration. +\li map a segmentation created on I1 also onto I2 with the same registration. +\li map a point set of image I1 also onto I2 with the same registration. \section MAP_MAPPER_Usage Usage -To use the mapper at least the input (image or point set) must be selected. Additionally you may select a registration object and, in case the input is an image, an optional reference image. +To use the mapper at least the input (image or point set) must be selected. Additionally, you may select a registration object and, in case the input is an image, an optional reference image. The reference image defines the geometry (field of view, orientation, spacing) that should be used for the result image. The view will try to automatically determine the reference image. By default it is the target image that was used to determine the selected registration. If auto selection cannot determine the reference (e.g. because it was not specified or it is currently not loaded), the input image will be selected as reference. The reference image can be also defined by the user explicitly by activating manual selection. -REMARK: If you map point sets you can ignore the reference image slot. It has no affect. +REMARK: If you map point sets you can ignore the reference image slot. It has no effect. REMARK: The mapping results will be added as child nodes to the used input node. REMARK: If you do not select an registration the view will assume that you make an identity transform. This is a convenient way if you just want to resample an image into the geometry of an other image (when no registration is needed). Also in this use case you can take advantage of the different interpolation and sub/super sampling strategies. \imageMacro{map_mapper_instructions.png, "Main elements of the mapper view.", 7} (1) The currently selected registration that will be used for mapping. Click to change.\n (2) Reset button that will remove the current selected registration and switch back to an identity transform.\n (3) The currently selected input data, that will be mapped. Click to change.\n (4) The currently (automatically or by user) selected reference image, that defines the geometry of the result. Click to change.\n (5) The name of the result data in the data manager.\n (6) The start button(s) to commence the mapping process. For details regarding the two options see \ref MAP_MAPPER_Refine.\n (7) Log windows with messages regarding the mapping process.\n \section MAP_MAPPER_Refine Mapping or geometry refinement The mapper view offers two options to map images:\n \li "Map" (default) \li "Refine geometry" -For images "Map" fills the pixels of the output image by interpolating input image pixels using the registration object. This option always works. -But may take longer and introduces interpolation errors, because a new image is resampled.\n -The second option "Refine geometry" is only offered, if the registration (more precise its inverse kernel) is matrix based and the selected data is an image. +For images "Map" fills the pixels of the output image by interpolating input image pixels using the registration object. This option always works, +but may take longer and introduces interpolation errors, because a new image is resampled.\n +The second option "Refine geometry" is only offered, if the registration (more precisely its inverse kernel) is matrix based and the selected data is an image. In this case it just clones the image and refines its image geometry (origin and orientation) to project it to the position indicated by the registration; thus no interpolation artefacts are introduced. \remark If you want to use a mapped image in conjunction with the image statistics plugin and a mask of the reference image (or you want to proceed any other computation that expects the voxel to be in the same grid for direct numeric comparison), you must use "Map" to ensure the same geometry (including the same image grid; including same spacing and resolution). Otherwise operations like the images statistics plugin will fail. \section MAP_MAPPER_Settings Settings \imageMacro{map_mapper-settings.png, "Available settings for mapping images.", 7} If you map the image (and not just refine the geometry), you have several settings available:\n \li "Allow undefined pixels": Activate to handle pixels of the result image that are not in the field of view of the input image. These pixels will get the "padding value". -\li "Allow error pixels": Activate to handle pixels of the result image that can not be mapped because the registration does not support this part of the output image. These pixels will get the "error value". +\li "Allow unregistered pixels": Activate to handle pixels of the result image that can not be mapped because the registration does not support this part of the output image. These pixels will get the "error value". \li "Interpolator": Set to choose the interpolation strategy that should be used for mapping. \li "Activate super/sub sampling": Activate if you want to use origin and orientation of the reference image but want to alter the spacing. \section MAP_MAPPER_Interpolation Interpolation You can choose from the following interpolation strategies:\n -\li "nearest neighbor": Use the value of the nearest pixel. Fastest, but high interpolation errors for gray value images. Right choice for label images or masks. +\li "Nearest Neighbor": Use the value of the nearest pixel. Fastest, but high interpolation errors for gray value images. Right choice for label images or masks. \li "Linear": Fast linear interpolation with often sufficient quality. Tends to blur edges. \li "BSpline (3rd order)": Good trade off between time and quality. \li "Windowed Sinc (Hamming)": Good interpolation quality but very time consuming. \li "Windowed Sinc (Welch)": Good interpolation quality but very time consuming. \section MAP_MAPPER_Masks Handling of masks/segmentations If you select an mask as input image, the plugin will be automatically reconfigured to settings that are suitable for the task of mapping masks. -Most importantly the interpolator will be set to "nearest neighbor". +Most importantly the interpolator will be set to "Nearest Neighbor". */ diff --git a/Plugins/org.mitk.gui.qt.matchpoint.mapper/src/internal/QmitkMatchPointMapper.ui b/Plugins/org.mitk.gui.qt.matchpoint.mapper/src/internal/QmitkMatchPointMapper.ui index fe48fae142..a1da741298 100644 --- a/Plugins/org.mitk.gui.qt.matchpoint.mapper/src/internal/QmitkMatchPointMapper.ui +++ b/Plugins/org.mitk.gui.qt.matchpoint.mapper/src/internal/QmitkMatchPointMapper.ui @@ -1,565 +1,565 @@ MatchPointMapperControls 0 0 392 816 5 5 5 5 5 Selected registration: 3 0 40 Input data (images or point sets): 3 0 40 Select reference manually instat of determined by registration... Select reference image manually: 3 0 40 true 0 false false false Execution 5 5 5 5 5 Mapped data name: Name of the resulting mapped image mappedImage <html><head/><body><p>Starts the mapping of the input image with the current settings.</p></body></html> Map false <html><head/><body><p>Applys the registration by refining the geometry of the data and not by resampling it. This option can only be selected if the chosen registration is 3D and can be decomposed in a rotation matrix and offset.</p></body></html> Refine geometry Log: 9 true QTextEdit::NoWrap true Clear log on mapping start Settings 5 5 5 5 5 0 0 50 false <html><head/><body><p>Allows that pixels may not be defined in the mapped image because they are outside of the field of view of the used input image.</p><p>The pixels will be marked with the given padding value.</p><p>If unchecked the mapping will be aborted in a case of undefined pixels.</p></body></html> Allow undefined pixels false true 5 9 5 9 5 Padding value: 0 0 Pixel value that indicates pixels that are outside of the input image -5000 5000 0 0 50 false <html><head/><body><p>Allows that pixels may not be registred because they are outside of the field of view of the used registration. The location in the correlated input image pixel(s) are therefore unkown. The pixels will be marked witrh the given error value.</p><p>If unchecked the mapping will be aborted in a case of unregistered pixels.</p></body></html> - Allow unregistred pixels + Allow unregistered pixels false true 5 5 5 Error value: 0 0 <html><head/><body><p>Value of pixels that cannot be registered because of an unsufficient field of view of the selected registration instance.</p></body></html> -5000 5000 0 0 Interpolator: true <html><head/><body><p>Interpolation function that should be used to map the pixel values from the input image into the result image.</p></body></html> 1 Nearest Neighbor Linear BSpline (3rd order) Windowed Sinc (Hamming) Windowed Sinc (Welch) 0 0 Activate super/sub sampling true false 5 <html><head/><body><p>Check to ensure that x, y and z dimension use the same sampling factor.</p></body></html> linked factors true 0 0 x: Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter <html><head/><body><p>Indicate the sampling factor to change the resolution.</p><p>2.0: doubled resolution; e.g. 100 pixels -&gt; 200 pixels and spacing 1 -&gt; spacing 0.5</p><p>0.5: half resolution; e.g. 100 pixels -&gt; 50 pixels and spacing 1 -&gt; spacing 2</p></body></html> y: Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter z: Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter Qt::Vertical QSizePolicy::Preferred 20 700 QmitkSingleNodeSelectionWidget QWidget
QmitkSingleNodeSelectionWidget.h
1 5 5 true true true diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox index a2cc0e7284..69c9dce5b6 100644 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox +++ b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox @@ -1,41 +1,41 @@ /** \page org_mitk_gui_qt_mitkphenotyping The Phenotyping \tableofcontents MITK Phenotyping is a selection of algorithms that can be used to extract image-based phenotypes, for example using a radiomics approach. The software is part of the research of the Division of Medical Image Computing of the German Cancer Research Center (DKFZ). MITK Phenotyping is not intended to be a single application, it is rather a collection of the necessary plugins within the offical MITK releases. The functionality of MITK Phenotyping can be accessed in different ways: Using the graphical interface using the Plugins listed below, using command line applications, or using one of the programming interfaces. \section org_mitk_gui_qt_mitkphenotyping_Tutorials Tutorials \li \subpage org_mitk_views_radiomicstutorial_gui_portal A tutorial on how to use the grapical interface of MITK Phenotying \section org_mitk_gui_qt_mitkphenotyping_Views Views \subsection sub2 Specific Views: Views that were developed with the main focus on Radiomics. They still might be used in other use-cases as well: \li \subpage org_mitk_views_radiomicstransformationview : Image transformations like Resampling, Laplacian of Gaussian, and Wavelet Transformations \li \subpage org_mitk_views_radiomicsmaskprocessingview : Processing and Cleaning of Masks \li \subpage org_mitk_views_radiomicsarithmetricview : Processing images using mathematical operations \li \subpage org_mitk_views_radiomicsstatisticview : Calculate Radiomics Features \subsection sub1 Non-Specific Views: This section contains views that are included within MITK Phenotyping, but were developed with a broader application in mind. \li \subpage org_mitk_views_basicimageprocessing : Deprecated plugin for performing different image-related tasks like subtraction, mutliplaction, filtering etc. -\li \subpage org_mitk_gui_qt_matchpoint_algorithm_browser : Selection of MatchPoint (Registration) Algorithm -\li \subpage org_mitk_gui_qt_matchpoint_algorithm_control : Configuring and Controlling MatchPoint (Registration) Algorithm -\li \subpage org_mitk_gui_qt_matchpoint_evaluator : Evaluate the Registration performance using MatchPoint -\li \subpage org_mitk_gui_qt_matchpoint_manipulator : Adapt a registration calculated using MatchPoint -\li \subpage org_mitk_gui_qt_matchpoint_mapper : Apply a MatchPoint Registration to a specific image -\li \subpage org_mitk_gui_qt_matchpoint_visualizer : Visualize a Registration obtained with MatchPoint -\li \subpage org_mitk_gui_qt_matchpoint_algorithm_batch : Running MatchPoint over multiple images (BatchMode) +\li \subpage org_mitk_views_matchpoint_algorithm_browser : Selection of MatchPoint (Registration) Algorithm +\li \subpage org_mitk_views_matchpoint_algorithm_control : Configuring and Controlling MatchPoint (Registration) Algorithm +\li \subpage org_mitk_views_matchpoint_evaluator : Evaluate the Registration performance using MatchPoint +\li \subpage org_mitk_views_matchpoint_manipulator : Adapt a registration calculated using MatchPoint +\li \subpage org_mitk_views_matchpoint_mapper : Apply a MatchPoint Registration to a specific image +\li \subpage org_mitk_views_matchpoint_visualizer : Visualize a Registration obtained with MatchPoint +\li \subpage org_mitk_views_matchpoint_algorithm_batch : Running MatchPoint over multiple images (BatchMode) \li \subpage org_mitk_views_multilabelsegmentation : Create and editing of Multilabel-Segmentations. \li \subpage org_mitk_views_segmentation : Create simple segmentations \li \subpage org_mitk_views_segmentationutilities : Utilities for the processing of simple segmentations. \section radiomics_miniapps MiniApps (Command line Tools) \li \subpage MiniAppExplainPage Explanation of the Command Line App concept in MITK \li \subpage mitkBasicImageProcessingMiniAppsPortalPage : List of common preprocessing MiniApps \li \subpage mitkClassificationMiniAppsPortalPage : (Incomplete) list of MITK Classification MiniApps */ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/doxygen/tutorial_gui/RadiomicsTutorial_GUI_03_Preprocessing.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/doxygen/tutorial_gui/RadiomicsTutorial_GUI_03_Preprocessing.dox index 52194c1bcd..33b6af06c5 100644 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/doxygen/tutorial_gui/RadiomicsTutorial_GUI_03_Preprocessing.dox +++ b/Plugins/org.mitk.gui.qt.radiomics/documentation/doxygen/tutorial_gui/RadiomicsTutorial_GUI_03_Preprocessing.dox @@ -1,23 +1,23 @@ /** \page org_mitk_views_radiomicstutorial_gui_03_preprocessing GUI based Radiomics Tutorial - Preprocessing the data \subsection Preprocessing the data The first step we take is to resample the data. To do so, we open the "Radiomics Transformation" View and select the "Resample Image" panel. We start by resampling the original image and therefore select the original picture (for us, Pic3D). Right-clicking on the image in the "Data Manager" and selecting the option "Details" gives us more information on the image. As we can see, our image has a spacing of [1, 1, 3], with an inplane resolution of 1x1mm and a out-of-plane resolution of 3 mm. We therefore decide to resample the image to an isotropic resolution of 1x1x1 mm. \imageMacro{RadiomicsTutorial_GUI_Step3_01_DetailView.png,"Details showing the spacing of the original image.",1} -To resample the image, we de-select "Dimension X" and "Dimension Y" option and set the "Dimension Z" option to 1, as indiciated by the image above. This tells the resampling algorithm to change only the last dimension to the value we specified. We further select to have the output image as double and chose B-Spline as resampling algorithm. This is a fast and still accurate option for resampling. To learn more about the other interpolation modes, refer to \ref org_mitk_gui_qt_matchpoint_mapper . +To resample the image, we de-select "Dimension X" and "Dimension Y" option and set the "Dimension Z" option to 1, as indiciated by the image above. This tells the resampling algorithm to change only the last dimension to the value we specified. We further select to have the output image as double and chose B-Spline as resampling algorithm. This is a fast and still accurate option for resampling. To learn more about the other interpolation modes, refer to \ref org_mitk_views_matchpoint_mapper . After resampling the original image, we also need to resample the segmentation. For this, we select the segmentation, leave the dimensions unchanged. Remove the "Output as double" option, as segmentations are not double values and choose a linear interpolation, which seems to be a better solution for resampling masks. We also check the option that we are resampling a mask. After performing those two steps, there should be two additional, resampled images in the "Data Manager". As a second step, we calculate some Laplacian of Gaussian images of the resampled image that allow us to capture more detailed information. For this, we select the panel "Laplacian of Gaussian" of the "Radiomics Transformation"-view and perform the algorithm three times with different sigma values (we chose 1,2, and 4). Make sure that you selected the right image to calculate the image, i.e. the resampled image. Finally, we clear the mask to obtain a clear segmentation of the target structure and remove possible resampling artifacts. To do so, we open the "Radiomics Mask Processing" View and select the resampled image and the resampled mask. We then select a lower limit only and set it to 160. Since we are working with MR, this is not a fixed value but something we manually determined. With this set, we perform the mask reducing by cklicking "Clean Mask based on Intervall". After this, we have the resampled image, three LoG images and a resampled and cleaned mask. The result should look similar to the next picture. You can also see the final image structure we obtained from our processing. It might help you to compare your results, although it is not necessary to obtain the same structure as long as you have all necessary images. \imageMacro{RadiomicsTutorial_GUI_Step3_02_FinishedPreprocessing.png,"Final results with a completed resampling",1} */ \ No newline at end of file