DOC: Link groups in Doxygen in module description

Create links to referenced groups when generating the Doxygen
documentation from the module description.

Doxygen grouping documentation:
https://www.doxygen.nl/manual/grouping.html

CMake/ITKGroups.cmake

@@ -32,13 +32,14 @@ create a processing pipeline.")
 
 set(Registration_documentation "This group of modules address the registration
 problem: find the spatial transformation between two images. This is a high
-level group that makes use of many lower level modules such as ITKTransform,
-ITKOptimizers, ITKFiniteDifference, and ITKFEM.")
+level group that makes use of many lower level modules such as
+\ref ITKTransform, \ref ITKOptimizers, \ref ITKFiniteDifference, and
+\ref ITKFEM.")
 
 set(Segmentation_documentation "This group of modules address the segmentation
 problem: partition the image into classified regions (labels). This is a high
-level group that makes use of many lower level modules such as ITKQuadEdgeMesh
-and ITKNarrowBand.")
+level group that makes use of many lower level modules such as
+\ref ITKQuadEdgeMesh and \ref ITKNarrowBand.")
 
 set(Numerics_documentation "This group of modules are basic numerical tools and
 algorithms that have general applications outside of imaging.")

Modules/Filtering/AnisotropicSmoothing/itk-module.cmake

@@ -2,8 +2,8 @@ set(DOCUMENTATION "This module contains filters that implement variations of
 anisotropic smoothing. This is an image denoising technique that strives for
 preserving edges on the images while smoothing regions of uniform intensity.
 This type of filtering is convenient as a preprocessing stage of segmentation
-algorithms. You may find useful as well the filters in the ITKCurvatureFlow
-module and the ITKSmoothingModule.")
+algorithms. You may find useful as well the filters in the \ref ITKCurvatureFlow
+module and the \ref ITKSmoothingModule.")
 
 itk_module(ITKAnisotropicSmoothing
   COMPILE_DEPENDS

Modules/Filtering/GPUAnisotropicSmoothing/itk-module.cmake

@@ -3,7 +3,7 @@ implement variations of anisotropic smoothing. This is an image denoising
 technique that strives for preserving edges on the images while smoothing regions
 of uniform intensity.  This type of filtering is convenient as a preprocessing
 stage of segmentation algorithms. You may find useful as well the filters in the
-ITKGPUSmoothingModule.")
+\ref ITKGPUSmoothingModule.")
 
 itk_module(ITKGPUAnisotropicSmoothing
   DEPENDS

Modules/Filtering/GPUSmoothing/itk-module.cmake

@@ -1,7 +1,7 @@
 set(DOCUMENTATION "This module contains the GPU implementation of the
 most common image smoothing filters.  For example, Gaussian and Median
 filters.  You may also find it interesting to look at the
-ITKAnisotropicSmoothing group of filters.")
+\ref ITKAnisotropicSmoothing group of filters.")
 
 itk_module(ITKGPUSmoothing
   DEPENDS

Modules/Filtering/Smoothing/itk-module.cmake

@@ -1,6 +1,6 @@
 set(DOCUMENTATION "This module includes the most common image smoothing
 filters. For example, Gaussian and Median filters. You may also find it
-interesting to look at the ITKAnisotropicSmoothing group of filters.")
+interesting to look at the \ref ITKAnisotropicSmoothing group of filters.")
 
 itk_module(ITKSmoothing
   ENABLE_SHARED

Modules/Registration/Common/itk-module.cmake

@@ -6,9 +6,9 @@ registration procedure.  There are classes to perform multi-resolution image
 registration and also classes to registrations other that image-to-image
 registrations, e.g. point set-to-image  or point set-to-point set
 registrations.  Transforms used in the registration can be found in
-ITKTransform, and optimizers can be found in ITKOptimizers. To compare the
-moving image to the fixed image with the image metric, an interpolator is
-required-- these can be found in ITKImageFunction.")
+\ref ITKTransform, and optimizers can be found in \ref ITKOptimizers. To
+compare the moving image to the fixed image with the image metric, an
+interpolator is required-- these can be found in \ref ITKImageFunction.")
 
 if(BUILD_EXAMPLES)
 set(EXAMPLE_TEST_CASE_DEPENDANCIES

Modules/Segmentation/Classifiers/itk-module.cmake

@@ -2,7 +2,8 @@ set(DOCUMENTATION "This module contains algorithms to classify pixels in an
 image. It can be used, for example, to identify pixel membership within a set
 of tissue types. Different algorithms are available including Bayesian
 classification, Gaussian models, and K-means clustering. After tissue labels
-have been assigned, they can be modified and applied with the ITKLabelMap.")
+have been assigned, they can be modified and applied with the
+\ref ITKLabelMap.")
 
 itk_module(ITKClassifiers
   DEPENDS

Modules/Segmentation/ConnectedComponents/itk-module.cmake

@@ -1,7 +1,7 @@
 set(DOCUMENTATION "This module contains modules to identify and modify connected
 components. Theses algorithms are commonly applied to binary or label map
-images. See also ITKClassifiers, ITKLabelMap, and
-ITKBinaryMathematicalMorphology.")
+images. See also \ref ITKClassifiers, \ref ITKLabelMap, and
+\ref ITKBinaryMathematicalMorphology.")
 
 itk_module(ITKConnectedComponents
   DEPENDS

Modules/Segmentation/KLMRegionGrowing/itk-module.cmake

@@ -1,7 +1,7 @@
 set(DOCUMENTATION "This module contains classes to perform energy-based region
 growing for multiband images.  Since this is based on G. Koepfler, C. Lopez and
 J. M. Morel's work, the acronym KLM is added to quality the region growing
-method. See also ITKRegionGrowing.")
+method. See also \ref ITKRegionGrowing.")
 
 itk_module(ITKKLMRegionGrowing
   ENABLE_SHARED

Modules/Segmentation/LabelVoting/itk-module.cmake

@@ -2,7 +2,8 @@ set(DOCUMENTATION "This module contains filters that perform label voting, i.e.
 they count the number of pixels with a given label within a neighborhood and
 determine the output pixel based on the count. The operations on label images
 are similar to filtering on scalar images. See also
-ITKBinaryMathematicalMorphology, ITKConnectedComponents, and ITKLabelMap.")
+\ref ITKBinaryMathematicalMorphology, \ref ITKConnectedComponents, and
+\ref ITKLabelMap.")
 
 itk_module(ITKLabelVoting
   DEPENDS

Modules/Segmentation/MarkovRandomFieldsClassifiers/itk-module.cmake

@@ -1,7 +1,7 @@
 set(DOCUMENTATION "This module contains classes to perform Markov Random Field
 classification of image pixels.  An initial label image, perhaps generated by
-ITKClassifiers, is improved by iteratively accounting for the spatial coherence
-of the labels.")
+\ref ITKClassifiers, is improved by iteratively accounting for the spatial
+coherence of the labels.")
 
 itk_module(ITKMarkovRandomFieldsClassifiers
   ENABLE_SHARED

Modules/Segmentation/RegionGrowing/itk-module.cmake

@@ -1,7 +1,7 @@
 set(DOCUMENTATION "This module contains classes to perform the region growing
 approach to image segmentation.  A seed pixel is iteratively propagated to a
 region identifying a tissue type by testing if connected pixels pass a criteria.
-See also ITKKLMRegionGrowing.")
+See also \ref ITKKLMRegionGrowing.")
 
 itk_module(ITKRegionGrowing
   ENABLE_SHARED