COMP: Use @ref for module Doxygen cross-refs to silence CMP0219

Modules/Filtering/AnisotropicSmoothing/itk-module.cmake

@@ -4,7 +4,7 @@ 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
-\\\\ref ITKCurvatureFlow and \\\\ref ITKSmoothing.")
+@ref ITKCurvatureFlow and @ref ITKSmoothing.")
 
 itk_module(
   ITKAnisotropicSmoothing

Modules/Filtering/CurvatureFlow/itk-module.cmake

@@ -3,7 +3,7 @@ set(DOCUMENTATION
 Curvature Flow. This is a technique that uses an iterative solution of partial
 differential equations to implement image denoising image filtering. These
 classes are typically used as edge-preserving smoothing filters. You may also
-find the \\\\ref ITKSmoothing and the \\\\ref ITKAnisotropicSmoothing useful
+find the @ref ITKSmoothing and the @ref ITKAnisotropicSmoothing useful
 as well.")
 
 itk_module(

Modules/Filtering/GPUAnisotropicSmoothing/itk-module.cmake

@@ -4,7 +4,7 @@ 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
-\\\\ref ITKGPUSmoothing.")
+@ref ITKGPUSmoothing.")
 
 itk_module(
   ITKGPUAnisotropicSmoothing

Modules/Filtering/GPUSmoothing/itk-module.cmake

@@ -2,7 +2,7 @@ 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
-\\\\ref ITKAnisotropicSmoothing group of filters.")
+@ref ITKAnisotropicSmoothing group of filters.")
 
 itk_module(
   ITKGPUSmoothing

Modules/Filtering/Smoothing/itk-module.cmake

@@ -1,7 +1,7 @@
 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 \\\\ref ITKAnisotropicSmoothing group of filters.")
+interesting to look at the @ref ITKAnisotropicSmoothing group of filters.")
 
 itk_module(
   ITKSmoothing

Modules/Registration/Common/itk-module.cmake

@@ -7,9 +7,9 @@ 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
-\\\\ref ITKTransform, and optimizers can be found in \\\\ref ITKOptimizers. To
+@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.")
+interpolator is required-- these can be found in @ref ITKImageFunction.")
 
 if(BUILD_EXAMPLES)
   set(EXAMPLE_TEST_CASE_DEPENDANCIES

Modules/Segmentation/Classifiers/itk-module.cmake

@@ -4,7 +4,7 @@ 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
-\\\\ref ITKLabelMap.")
+@ref ITKLabelMap.")
 
 itk_module(
   ITKClassifiers

Modules/Segmentation/ConnectedComponents/itk-module.cmake

@@ -1,8 +1,8 @@
 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 \\\\ref ITKClassifiers, \\\\ref ITKLabelMap, and
-\\\\ref ITKBinaryMathematicalMorphology.")
+images. See also @ref ITKClassifiers, @ref ITKLabelMap, and
+@ref ITKBinaryMathematicalMorphology.")
 
 itk_module(
   ITKConnectedComponents

Modules/Segmentation/KLMRegionGrowing/itk-module.cmake

@@ -2,7 +2,7 @@ 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 \\\\ref ITKRegionGrowing.")
+method. See also @ref ITKRegionGrowing.")
 
 itk_module(
   ITKKLMRegionGrowing

Modules/Segmentation/LabelVoting/itk-module.cmake

@@ -3,8 +3,8 @@ set(DOCUMENTATION
 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
-\\\\ref ITKBinaryMathematicalMorphology, \\\\ref ITKConnectedComponents, and
-\\\\ref ITKLabelMap.")
+@ref ITKBinaryMathematicalMorphology, @ref ITKConnectedComponents, and
+@ref ITKLabelMap.")
 
 itk_module(
   ITKLabelVoting

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
-\\\\ref ITKClassifiers, is improved by iteratively accounting for the spatial
+@ref ITKClassifiers, is improved by iteratively accounting for the spatial
 coherence of the labels.")
 
 itk_module(

Modules/Segmentation/RegionGrowing/itk-module.cmake

@@ -2,7 +2,7 @@ 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 \\\\ref ITKKLMRegionGrowing.")
+See also @ref ITKKLMRegionGrowing.")
 
 itk_module(
   ITKRegionGrowing