diff --git a/Documentation/Doxygen/2-UserManual/MiniApps.dox b/Documentation/Doxygen/2-UserManual/MiniApps.dox index eab8f29951..40a3835f29 100644 --- a/Documentation/Doxygen/2-UserManual/MiniApps.dox +++ b/Documentation/Doxygen/2-UserManual/MiniApps.dox @@ -1,59 +1,61 @@ /** \page MiniAppExplainPage MITK MiniApps \section MiniAppExplainPageDescription What are MiniApps MiniApps are small command line tools. Generally the purpose of each of these tools is designed to fulfill one simple task, e.g. resample an image or extract image statistics of a given region of interest (ROI). They are intended to provide command line access to a variety of features of MITK, thus facilitating batched processing of data. \section MiniAppExplainPageUsage Usage Each MiniApp should provide information about its usage. If it is called without parameters it will output help information about expected inputs, outputs and parameters. Below you can see the help output of the MitkGibbsTracking MiniApp: \code $./MitkGibbsTracking -i, --input, input image (tensor, ODF or FSL/MRTrix SH-coefficient image) -p, --parameters, parameter file (.gtp) -m, --mask, binary mask image (optional) -s, --shConvention, sh coefficient convention (FSL, MRtrix) (optional), (default: FSL) -o, --outFile, output fiber bundle (.fib) -f, --noFlip, do not flip input image to match MITK coordinate convention (optional) \endcode \section MiniAppExplainPageWorkbenchIntegration Integrating a command line tool into MITK Workbench The executable file has be to be announced in MITK Workbench. This can be done in Preferences window: Click 'Window' -> 'Preferences', and select 'Command Line Modules'. You can add directories containing executable files or you can select single executable files. Click 'OK' button. The configured command line tools are now available via the drop-down box of the Command Line Modules tab. \warning The build configuration of your MiniApp should match the build configuration of your MITK application. This is especially relevant for developers. Combining a Release application and Debug MiniApp or vice versa might not work. \section MiniAppExplainPageAvailableList Available MiniApps \li \subpage DiffusionMiniApps +\li \subpage mitkBasicImageProcessingMiniAppsPortalPage +\li \subpage mitkClassificationMiniAppsPortalPage \section MiniAppExplainPageTechnicalInformation Technical Information MiniApps follow the Slicer Execution Model in describing themselves via xml: \code $./GibbsTracking --xml Fiber Tracking and Processing Methods Gibbs Tracking MBI ... \endcode \note Full conformity is still a work in progress. */ diff --git a/Modules/BasicImageProcessing/MiniApps/ForwardWavelet.cpp b/Modules/BasicImageProcessing/MiniApps/ForwardWavelet.cpp index 3b5630160b..4ff8e80fde 100644 --- a/Modules/BasicImageProcessing/MiniApps/ForwardWavelet.cpp +++ b/Modules/BasicImageProcessing/MiniApps/ForwardWavelet.cpp @@ -1,141 +1,142 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #include "mitkProperties.h" #include "mitkCommandLineParser.h" #include "mitkIOUtil.h" #include static bool ConvertToBool(std::map &data, std::string name) { if (!data.count(name)) { return false; } try { return us::any_cast(data[name]); } catch (us::BadAnyCastException &) { return false; } } int main(int argc, char* argv[]) { mitkCommandLineParser parser; parser.setTitle("Multi-Resolution Pyramid"); parser.setCategory("Basic Image Processing"); parser.setDescription(""); parser.setContributor("MBI"); parser.setArgumentPrefix("--", "-"); // Add command line argument names parser.addArgument("help", "h", mitkCommandLineParser::Bool, "Help:", "Show this help text"); parser.addArgument("image", "i", mitkCommandLineParser::InputFile, "Input image:", "Input Image", us::Any(), false); parser.addArgument("output", "o", mitkCommandLineParser::OutputFile, "Output file:", "Output Mask", us::Any(), false); parser.addArgument("output-extension", "e", mitkCommandLineParser::OutputFile, "Output file:", "Output Mask", us::Any(), false); parser.addArgument("number-of-levels", "levels", mitkCommandLineParser::Int, "Numbers of pyramid levels", "Number of pyramid levels", us::Any(), false); parser.addArgument("number-of-bands", "bands", mitkCommandLineParser::Int, "Numbers of pyramid levels", "Number of pyramid levels", us::Any(), false); parser.addArgument("wavelet", "w", mitkCommandLineParser::Int, "0: Shannon, 1: Simocelli, 2: Vow, 3: Held", "0: Shannon, 1: Simocelli, 2: Vow, 3: Held", us::Any(), false); parser.addArgument("border-condition", "border", mitkCommandLineParser::Int, "0: Constant, 1: Periodic, 2: Zero Flux Neumann", "0: Constant, 1: Periodic, 2: Zero Flux Neumann", us::Any(), false); std::map parsedArgs = parser.parseArguments(argc, argv); if (parsedArgs.size() == 0) return EXIT_FAILURE; // Show a help message if (parsedArgs.count("help") || parsedArgs.count("h")) { std::cout << parser.helpText(); return EXIT_SUCCESS; } std::string inputFilename = us::any_cast(parsedArgs["image"]); std::string outputFilename = us::any_cast(parsedArgs["output"]); std::string outputExtension = us::any_cast(parsedArgs["output-extension"]); auto nodes = mitk::IOUtil::Load(inputFilename); if (nodes.size() == 0) { MITK_INFO << "No Image Loaded"; return 0; } mitk::Image::Pointer image = dynamic_cast(nodes[0].GetPointer()); if (image.IsNull()) { MITK_INFO << "Loaded data (image) is not of type image"; return 0; } - int levels = us::any_cast(parsedArgs["number-of-levels"]); - int bands = us::any_cast(parsedArgs["number-of-bands"]); + int levels = us::any_cast(parsedArgs["number-of-levels"]); + int bands = us::any_cast(parsedArgs["number-of-bands"]); mitk::BorderCondition condition = mitk::BorderCondition::Constant; mitk::WaveletType waveletType = mitk::WaveletType::Held; switch (us::any_cast(parsedArgs["wavelet"])) { case 0: waveletType = mitk::WaveletType::Shannon; break; case 1: waveletType = mitk::WaveletType::Simoncelli; break; case 2: waveletType = mitk::WaveletType::Vow; break; case 3: waveletType = mitk::WaveletType::Held; break; default: waveletType = mitk::WaveletType::Shannon; break; } switch (us::any_cast(parsedArgs["border-condition"])) { case 0: condition = mitk::BorderCondition::Constant; break; case 1: condition = mitk::BorderCondition::Periodic; break; case 2: condition = mitk::BorderCondition::ZeroFluxNeumann; break; default: condition = mitk::BorderCondition::Constant; break; } std::vector results = mitk::TransformationOperation::WaveletForward(image, levels, bands, condition, waveletType); unsigned int level = 0; for (auto image : results) { std::string name = outputFilename + us::Any(level).ToString() + outputExtension; + MITK_INFO << "Saving to " << name; mitk::IOUtil::Save(image, name); ++level; } return EXIT_SUCCESS; } diff --git a/Modules/BasicImageProcessing/documentation/UserManual/mitkBasicImageProcessingMiniAppsPortalPage.dox b/Modules/BasicImageProcessing/documentation/UserManual/mitkBasicImageProcessingMiniAppsPortalPage.dox new file mode 100644 index 0000000000..93627d40be --- /dev/null +++ b/Modules/BasicImageProcessing/documentation/UserManual/mitkBasicImageProcessingMiniAppsPortalPage.dox @@ -0,0 +1,45 @@ +/** +\page mitkBasicImageProcessingMiniAppsPortalPage MITK Basic Image Processing Mini Apps + +\tableofcontents + +The Basic Image Processing Mini Apps bundle the functionality that is commonly neeeded for the processing of medical images. As all other MiniApps, they follow the Slicer Execution Model in describing themselves via xml. You can simply obtain a description by calling the MiniApp without any parameter. If the MiniApp is calles with the option "--xml" a XML description of all possible parameter is added. + +\section bipmasec1 Description of Mini Apps + +\subsection bipmasub1 mitkFileConverter +Allows to convert a file from one type to another file type, for example to convert an image, saved in the nifti format to an image saved in the .nrrd format. + +\subsection bipmasub2 mitkForwardWavelet +Calculates the forward wavelet transformation of an image. The output will consist of multiple images, which will be saved in the format %id%, where and are specified by the user and %id% is a consecutive number. + +\subsection bipmasub3 mitkImageAndValueArithmetic +Mathematical operations with two operants, the individual voxels of the image and a specified floating point value. By default, the floating point value is the right operand. + +\subsection bipmasub4 mitkImageTypeCovnerter +Convert the data fromat that is used to save a voxel of an image. + +\subsection bipmasub5 mitkLaplacianOfGaussian +Calculate the Laplacian of Gaussian of an image with the specified sigma value. + +\subsection bipmasub6 mitkMaskOutlierFiltering +Can be used to clean an segmentation. The mean and standard deviation of the intensities which are masked is calculated and then all mask voxels are removed that cover image voxels which are not within a 3 sigma range. + +\subsection bipmasub7 mitkMaskRangeBasedFiltering +Removing all voxels from a mask that cover image voxels which are outside of a given range. The range can be either specified by a lower limit, a upper limit, or both at the same time. + +\subsection bipmasub8 mitkMultiResolutionPyramid +Calculate a Multi-Resolution Pyramid of the given image. The resolution is reduced by factor 2 for each step. + +\subsection bipmasub9 mitkResampleImage +Resample a mask to a new spacing + +\subsection bipmasub10 mitkResampleMask +Similar to mitkResampleImage, but specificly tailored to resampling a mask. + +\subsection bipmasub11 mitkSingleImageArithmetic +Applies single operand mathematical operations to each voxel of an image + +\subsection bipmasub12 mitkTwoImageArithmetic +Applied two operand mathematical operations to each voxels of two images. +*/ diff --git a/Modules/Classification/documentation/UserManual/mitkClassificationMiniAppsPortalPage.dox b/Modules/Classification/documentation/UserManual/mitkClassificationMiniAppsPortalPage.dox new file mode 100644 index 0000000000..2f8f90c12c --- /dev/null +++ b/Modules/Classification/documentation/UserManual/mitkClassificationMiniAppsPortalPage.dox @@ -0,0 +1,19 @@ +/** +\page mitkClassificationMiniAppsPortalPage MITK Classification Mini Apps + +\tableofcontents + +The Classification Mini Apps bundle the functionality that is commonly neeeded for the processing and learning with medical images. As all other MiniApps, they follow the Slicer Execution Model in describing themselves via xml. You can simply obtain a description by calling the MiniApp without any parameter. If the MiniApp is calles with the option "--xml" a XML description of all possible parameter is added. + +\section mcmasec1 Description of Mini Apps + +\subsection mcmasub1 mitkCLGLobalImageFeatures +Allows to calculate features that describe the masked area. Can be used to obtain radiomics features. + +\subsection mcmasub2 mitkCLN4 +Allows to calculate a bias normalization. + +\subsection mcmasub3 mitkCLStaple +Allows to combine multiple segmentations into a single segmentation using the STAPLE algorithm + +*/ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkDiffusionImagingPortalPage.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkDiffusionImagingPortalPage.dox deleted file mode 100644 index d2052b2ff2..0000000000 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkDiffusionImagingPortalPage.dox +++ /dev/null @@ -1,48 +0,0 @@ -/** -\page org_mitk_gui_qt_diffusionimaging MITK Diffusion - -\tableofcontents - -MITK Diffusion offers a selection of image analysis methods for dMRI processing. It encompasses the research of the Division of Medical Image Computing of the German Cancer Research Center (DKFZ). - -\section org_mitk_gui_qt_diffusionimagingComponents Components - -MITK Diffusion consists of multiple components with their own documentation: - -\subsection sub1 Data formats, import and export -\li \subpage QmitkDiffusionImagingDataImportPage - -\subsection sub2 Preprocessing and Reconstruction -\li \subpage org_mitk_views_diffusionpreprocessing -\li \subpage org_mitk_views_simpleregistrationview -\li \subpage org_mitk_views_headmotioncorrectionview -\li \subpage org_mitk_views_denoisingview -\li \subpage org_mitk_views_tensorreconstruction -\li \subpage org_mitk_views_qballreconstruction - -\subsection sub3 Visualization and Quantification -\li \subpage org_mitk_views_controlvisualizationpropertiesview -\li \subpage org_mitk_views_odfdetails -\li \subpage org_mitk_views_odfmaximaextraction -\li \subpage org_mitk_views_partialvolumeanalysisview -\li \subpage org_mitk_views_diffusionquantification -\li \subpage org_mitk_views_ivim - -\subsection sub4 Fiber Tractography -\li \subpage org_mitk_views_streamlinetracking -\li \subpage org_mitk_views_gibbstracking -\li \subpage org_mitk_views_mlbtview -\li \subpage org_mitk_views_fiberprocessing -\li \subpage org_mitk_views_fiberquantification -\li \subpage org_mitk_views_fiberfit -\li \subpage org_mitk_views_fiberclustering - -\subsection sub5 Fiberfox dMRI Simulation -\li \subpage org_mitk_views_fiberfoxview -\li \subpage org_mitk_views_fieldmapgenerator - -\subsection sub6 TBSS and Connectomics -\li \subpage org_mitk_views_tractbasedspatialstatistics -\li \subpage org_mitk_diffusionimagingapp_perspectives_connectomics - -*/ 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 9920021448..e9d319220e 100644 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox +++ b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/QmitkPhenotypingPortalPage.dox @@ -1,36 +1,41 @@ /** \page org_mitk_gui_qt_mitkphenotyping MITK 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_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/UserManual/old/Data_CSD.png b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/Data_CSD.png deleted file mode 100644 index 1eb60c1707..0000000000 Binary files a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/Data_CSD.png and /dev/null differ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/Data_Tensors.png b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/Data_Tensors.png deleted file mode 100644 index 7f42f2108a..0000000000 Binary files a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/Data_Tensors.png and /dev/null differ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing.dox deleted file mode 100644 index bff25497fa..0000000000 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing.dox +++ /dev/null @@ -1,126 +0,0 @@ -/** -\page org_mitk_views_basicimageprocessing The Basic Image Processing Plugin - -\imageMacro{QmitkBasicImageProcessing_ImageProcessing_48.png,"Icon of the Basic Image Processing Plugin",2.00} - -\tableofcontents - -\section QmitkBasicImageProcessingUserManualSummary Summary - -This view 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 QmitkBasicImageProcessingUserManualOverview for more detailed information on usage and supported filters. -If you encounter problems using the view, please have a look at the \ref QmitkBasicImageProcessingUserManualTrouble page. - -\section QmitkBasicImageProcessingUserManualOverview Overview - -This view 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. -Currently the view 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). - -\imageMacro{QmitkBasicImageProcessing_BIP_Overview.png,"MITK with the Basic Image Processing view",16.00} - -This document will tell you how to use this view, 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

- -
    -
  • Noise Suppression
  • -
      -
    • Gaussian Denoising
    • -
    • Median Filtering
    • -
    • Total Variation Denoising
    • -
    - -
  • Morphological Operations
  • -
      -
    • Dilation
    • -
    • Erosion
    • -
    • Opening
    • -
    • Closing
    • -
    - -
  • %Edge Detection
  • -
      -
    • Gradient Image
    • -
    • Laplacian Operator (Second Derivative)
    • -
    • Sobel Operator
    • -
    - -
  • Misc
  • -
      -
    • Threshold
    • -
    • Image Inversion
    • -
    • Downsampling (isotropic)
    • -
    -
- -

\a Dual image operations

- -
    -
  • Image Arithmetics
  • -
      -
    • Add two images
    • -
    • Subtract two images
    • -
    • Multiply two images
    • -
    • Divide two images
    • -
    - -
  • Binary Operations
  • -
      -
    • Logical AND
    • -
    • Logical OR
    • -
    • Logical XOR
    • -
    -
- -\section QmitkBasicImageProcessingUserManualUsage Usage - -All you have to do to use a filter is to: -
    -
  • Load an image into MITK
  • -
  • Select it in data manager -
  • Select which filter you want to use via the drop down list
  • -
  • Press the execute button
  • -
-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.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing_BIP_Overview.png b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing_BIP_Overview.png deleted file mode 100644 index fc97b545a9..0000000000 Binary files a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing_BIP_Overview.png and /dev/null differ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing_ImageProcessing_48.png b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing_ImageProcessing_48.png deleted file mode 100644 index b0244a0320..0000000000 Binary files a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkBasicImageProcessing_ImageProcessing_48.png and /dev/null differ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingAppUserManual.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingAppUserManual.dox deleted file mode 100644 index 76d27ea5fd..0000000000 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingAppUserManual.dox +++ /dev/null @@ -1,8 +0,0 @@ -/** -\page org_mitk_gui_qt_diffusionimagingapp What is the Diffusion Imaging Application - -MITK Diffusion offers a selection of image analysis methods for dMRI processing. It encompasses the research of the Division of Medical Image Computing of the German Cancer Research Center (DKFZ). - -For a detailed description of MITK Diffusion refer to \ref org_mitk_gui_qt_diffusionimaging . - -*/ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingDataImportPage.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingDataImportPage.dox deleted file mode 100644 index 20f1b1a2f0..0000000000 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingDataImportPage.dox +++ /dev/null @@ -1,82 +0,0 @@ -/** -\page QmitkDiffusionImagingDataImportPage Data formats, import and export - -MITK Diffusion supports many standard image formats such as NIFTI, NRRD and DICOM as well as common tractography file formats such as vtk/fib, trk, tck, and tractography DICOM. By including multiple tractography file formats native to other tools (e.g. 3D Slicer, MRtrix, DIPY), MITK Diffusion integrates seamlessly in complex workflows involving other tools. Additional file types such as spherical-harmonic coefficient files and voxel-wise fiber orientation or peak images are compliant with MRtrix. - -Data can be loaded using the open file dialog via the menu bar or by simply dragging and dropping the file into the data manager or one of data display windows. - -MITK Diffusion uses a left-posterior-superior coordinate system convention. Other toolkits, e.g. MRtrix, use an RAS coordinate system. This can cause flips for example of the complete tractogram or of the voxel-wise peaks or ODFs. MITK Diffusion implements mechanisms to deal with this by automatically catching and converting some cases and by providing the tools to manually correct for it, but it is important to keep this issue in mind and to perform the data processing in a corresponding thorough manner. - -\section DWI_format Diffusion-weighted Images - -This section describes the different file formats of raw diffusion-weighted images that are supported by MITK Diffusion. -General points: -\li MITK Diffusion internally applies the image rotation matrix to the diffusion-gradient vectors while loading the image. The original gradient directions are retained and used when saving the image again. - -\subsection dwi_nrrd NRRD (.nrrd/.dwi) - -NRRD (http://teem.sourceforge.net/nrrd/format.html) is the default file format for saving diffusion-weighted images with MITK Diffusion. The gradient and b-value information is directly stored in the file header. The image information is stored as a 3D vector image. Storing the file as .dwi or .nrrd is equivalent, only the file ending is different. Diffusion-weighted images are discerned from other images by the tag "modality:=DWMRI" in the NRRD image header. The gradient information is stored in the NRRD header in the following way: - -DWMRI_b-value:=1000.000000 - -DWMRI_gradient_0000:=0.000000 0.000000 0.000000 - -DWMRI_gradient_0001:=0.000000 0.000000 1.000000 - -DWMRI_gradient_0002:=-0.051773 0.252917 0.966102 - -... - -The b-value of the individual gradient directions is encoded via the squared norm of the respective vector. The tag "DWMRI_b-value" defines which b-value corresponds to a vector with norm 1. - -In some cases, dMRI NRRD files contain an additional "measurement frame" matrix, which specifies an additional rotation of the gradient vectors. This matrix is automatically applied when loading the file. The original gradient directions are retained and used when saving the image again. - -\subsection dwi_nifti NIFTI (.nii/.ni.gz) - -Diffusion-weighted images can be saved and loaded as NIFTI files (NIFTI-1 https://nifti.nimh.nih.gov/nifti-1). The gradient vector information is stored in to separate files for b-values (filename.bvals) and gradient vectors (filename.bvecs). When loading a nifti file (.nii or .nii.gz), MITK Diffusion looks for these two additional files (.bval/.bvals and .bvec/.bvecs) and if they are found, MITK will offer to load the image as diffusion-weighted image. In contrast to the NRRD format, all gradient vectors should have a length of 1. The b-values are stored explicietly in the .bval/.bvals file. When loaded into MITK, this information is again encoded into the gradient vectors, as it is done in the NRRD file format. - -\subsection dwi_dicom DICOM - -MITK is capable of importing diffusion-weighted DICOM images from GE, Siemens and Philips. Writing images is DICOM format is NOT supported! Mosaic images can be directly converted to regular images during import. To load a dMRI DICOM, simply drag and drop any file of the series into the application. - -\section special_format Special Image Types - -\subsection DTI_format Diffusion Tensor Images (.dti) - -The default format for diffusion tensor images in MITK Diffusion is a 3D NRRD file with a "3D-symmetric-matrix" pixel type. A tensor is encoded as 6 float values. The file ending for diffusion tensor files is .dti. -MITK Diffusion is also able to read NIFTI DTI files (6 or 9 component format), which are for example generated by the Camino multi tensor reconstruction. To be recognized as DTI files, the .nii or .nii.gz files have to be renamed to .dti. - -\subsection ODF_format ODF Images (.odf, .qbi (deprecated) ) - -MITK stores ODFs as 252 float values spherically sampled from the continuous ODF. The sampling directions are generate by a 5-fold subdivisions of an icosahedron. ODF images are stored in NRRD file format with the ending .odf. The image information is stored as a 3D vector image with a vector length of 252. - -The specific ODF sampling directions can be found here. - -\subsection sh_format Spherical Harmonics -Many applications in dMRI are using spherical harmonics to store spherical functions such as ODFs. MITK Diffusion stores these files as 3D vector images (NRRD format), where the vector components represent the SH coefficients. While this format is different from e.g. the MRtrix SH file format, MITK Diffusion can convert from MRtrix SH files to MITK Diffusion SH files. For historical reasons, the conversion tool is located in the \ref org_mitk_views_odfmaximaextraction view. Simply load the MRtrix SH image, select it in the data manager, select the "MRtrix" spherical harmonics convention and click "Start SH Coefficient Import". - -\subsection peak_format Peak Images (.nii, .nii.gz, .nrrd) - -MITK Diffusion stores peak images, resulting for example from an ODF maxima extraction, as 4D float images. The peak vector components are stored in the 4th dimension, therefore dimension 4 always contains a multiple of 3 entries. This format is the same as the format used by MRtrix. - -\section Tract_format Tractography Formats - -MITK Diffusion supports multiple formats for tractography files that are commonly used in the dMRI community. As in all other toolkits, the fiber point coordinates are stored as physical/world coordinates without any additional transformation. - -\subsection tract_vtk VTK (.vtk/.fib) - -The default format for tractograms is VTK (vtkPolyData) with the file endings .fib or .vtk. The advantage of the VTK format is that it can store additional information such as fiber weights and fiber colors. By default, both are saved with the actual tract information. - -\subsection tract_trk TrackVis (.trk) - -TRK is the tractography file format used by TrackVis and DIPY. See http://www.trackvis.org/docs/?subsect=fileformat for a detailed description of a format. - -\subsection tract_tck MRtrix (.tck) - -MITK Diffusion is able to read the tck file format native to MRtrix. Writing this format is currently not supported. By default, MRtrix uses a RAS coordinate convention in contrast to the MITK Diffusion (and also ITK) convention of LPS. To compensate for this, the fiber coordinates are negated in the x and y dimension. - -\subsection tract_dicom DICOM - -Tractography DICOM files compliant with the supplement 181 of the DICOM standard can be read and written by MITK Diffusion. This format is for example supported by the neuronavigation software of Brainlab. -DICOM tags of the read tractogram can be manually set or modified using the "Properties" view in MITK Diffusion. To do this, enable the "Developer Mode" option in Window>Preferences>Properties>Developer Mode. Then select the fiber bundle in the data manager and select the "Base Data" property list in the corresponding combobox of the "Properties" view. -*/ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingPortalPage.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingPortalPage.dox deleted file mode 100644 index d2052b2ff2..0000000000 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingPortalPage.dox +++ /dev/null @@ -1,48 +0,0 @@ -/** -\page org_mitk_gui_qt_diffusionimaging MITK Diffusion - -\tableofcontents - -MITK Diffusion offers a selection of image analysis methods for dMRI processing. It encompasses the research of the Division of Medical Image Computing of the German Cancer Research Center (DKFZ). - -\section org_mitk_gui_qt_diffusionimagingComponents Components - -MITK Diffusion consists of multiple components with their own documentation: - -\subsection sub1 Data formats, import and export -\li \subpage QmitkDiffusionImagingDataImportPage - -\subsection sub2 Preprocessing and Reconstruction -\li \subpage org_mitk_views_diffusionpreprocessing -\li \subpage org_mitk_views_simpleregistrationview -\li \subpage org_mitk_views_headmotioncorrectionview -\li \subpage org_mitk_views_denoisingview -\li \subpage org_mitk_views_tensorreconstruction -\li \subpage org_mitk_views_qballreconstruction - -\subsection sub3 Visualization and Quantification -\li \subpage org_mitk_views_controlvisualizationpropertiesview -\li \subpage org_mitk_views_odfdetails -\li \subpage org_mitk_views_odfmaximaextraction -\li \subpage org_mitk_views_partialvolumeanalysisview -\li \subpage org_mitk_views_diffusionquantification -\li \subpage org_mitk_views_ivim - -\subsection sub4 Fiber Tractography -\li \subpage org_mitk_views_streamlinetracking -\li \subpage org_mitk_views_gibbstracking -\li \subpage org_mitk_views_mlbtview -\li \subpage org_mitk_views_fiberprocessing -\li \subpage org_mitk_views_fiberquantification -\li \subpage org_mitk_views_fiberfit -\li \subpage org_mitk_views_fiberclustering - -\subsection sub5 Fiberfox dMRI Simulation -\li \subpage org_mitk_views_fiberfoxview -\li \subpage org_mitk_views_fieldmapgenerator - -\subsection sub6 TBSS and Connectomics -\li \subpage org_mitk_views_tractbasedspatialstatistics -\li \subpage org_mitk_diffusionimagingapp_perspectives_connectomics - -*/ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingVisualization.dox b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingVisualization.dox deleted file mode 100644 index f111e4e593..0000000000 --- a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/QmitkDiffusionImagingVisualization.dox +++ /dev/null @@ -1,22 +0,0 @@ -/** -\page org_mitk_views_controlvisualizationpropertiesview Visualization Control Panel - -\section QmitkDiffusionImagingVisualizationSettings ODF Visualization - -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 ODF 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. - -This is how a visualization with activated glyphs should look like: - -\imageMacro{Data_CSD.png,"ODF image with glyph visibility toggled ON",1} - -\imageMacro{Data_Tensors.png,"Tensor image with glyph visibility toggled ON",1} - -\section QmitkDiffusionImagingTractVisualizationSettings Tractogram Visualization - -If a tractogram is selected in the data manager, this view enables to visualize the fibers as tubes or thick lines as well as to play with the 2D and 3D clipping of the fiber visualization. The 3D clipping works per tractogram individually, which enables nice visualization of e.g. a CST tract extending upwards out of the bottom half of a whole brain tractogram. - -\imageMacro{tract_visualization.png,"CST tube visualization with sagittal 3D clipping of the whole-brain tractogram.",1} - -*/ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/dicom1.png b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/dicom1.png deleted file mode 100644 index a11c978150..0000000000 Binary files a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/dicom1.png and /dev/null differ diff --git a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/tract_visualization.png b/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/tract_visualization.png deleted file mode 100644 index 9805ac1578..0000000000 Binary files a/Plugins/org.mitk.gui.qt.radiomics/documentation/UserManual/old/tract_visualization.png and /dev/null differ