Merge pull request #2172 from zivy/updateInsightJournalURL

DOC: Change URLs to DOIs.

Code/BasicFilters/json/AdditiveGaussianNoiseImageFilter.json

@@ -94,7 +94,7 @@
     }
   ],
   "briefdescription" : "Alter an image with additive Gaussian white noise.",
-  "detaileddescription" : "Additive Gaussian white noise can be modeled as:\n\n\\par \n\\f$ I = I_0 + N \\f$ \n\n\n\\par \nwhere \\f$ I \\f$ is the observed image, \\f$ I_0 \\f$ is the noise-free image and \\f$ N \\f$ is a normally distributed random variable of mean \\f$ \\mu \\f$ and variance \\f$ \\sigma^2 \\f$ :\n\n\n\\par \n\\f$ N \\sim \\mathcal{N}(\\mu, \\sigma^2) \\f$ \n\n\nThe noise is independent of the pixel intensities.\n\n\\author Gaetan Lehmann\n\n\nThis code was contributed in the Insight Journal paper \"Noise\nSimulation\". https://www.insight-journal.org/browse/publication/721",
+  "detaileddescription" : "Additive Gaussian white noise can be modeled as:\n\n\\par \n\\f$ I = I_0 + N \\f$ \n\n\n\\par \nwhere \\f$ I \\f$ is the observed image, \\f$ I_0 \\f$ is the noise-free image and \\f$ N \\f$ is a normally distributed random variable of mean \\f$ \\mu \\f$ and variance \\f$ \\sigma^2 \\f$ :\n\n\n\\par \n\\f$ N \\sim \\mathcal{N}(\\mu, \\sigma^2) \\f$ \n\n\nThe noise is independent of the pixel intensities.\n\n\\author Gaetan Lehmann\n\n\nThis code was contributed in the Insight Journal paper \"Noise\nSimulation\". https://doi.org/10.54294/vh6vbw",
   "itk_module" : "ITKImageNoise",
   "itk_group" : "ImageNoise",
   "in_place" : true

Code/BasicFilters/json/AggregateLabelMapFilter.json

@@ -31,7 +31,7 @@
     }
   ],
   "briefdescription" : "Collapses all labels into the first label.",
-  "detaileddescription" : "This filter takes a label map as input and visits the pixels of all labels and assigns them to the first label of the label map. At the end of the execution of this filter, the map will contain a single filter.\n\nThis implementation was taken from the Insight Journal paper: https://www.insight-journal.org/browse/publication/176 \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see ShapeLabelObject , RelabelComponentImageFilter",
+  "detaileddescription" : "This filter takes a label map as input and visits the pixels of all labels and assigns them to the first label of the label map. At the end of the execution of this filter, the map will contain a single filter.\n\nThis implementation was taken from the Insight Journal paper: https://doi.org/10.54294/q6auw4 \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see ShapeLabelObject , RelabelComponentImageFilter",
   "itk_module" : "ITKLabelMap",
   "itk_group" : "LabelMap",
   "in_place" : true

Code/BasicFilters/json/AreaClosingImageFilter.json

@@ -50,7 +50,7 @@
     }
   ],
   "briefdescription" : "Morphological closing by attributes.",
-  "detaileddescription" : "An attribute closing removes blobs according to criteria such as area. When applied to grayscale images they have the effect of filling valleys (regions darker than their surroundings) based on area while leaving the rest of the image unchanged.\n\nThis code was contributed in the Insight Journal paper\n\n\"Grayscale morphological attribute operations\" by Beare R. https://www.insight-journal.org/browse/publication/203 \n\n\\author Richard Beare. Department of Medicine, Monash University, Melbourne, Australia.",
+  "detaileddescription" : "An attribute closing removes blobs according to criteria such as area. When applied to grayscale images they have the effect of filling valleys (regions darker than their surroundings) based on area while leaving the rest of the image unchanged.\n\nThis code was contributed in the Insight Journal paper\n\n\"Grayscale morphological attribute operations\" by Beare R. https://doi.org/10.54294/ifvjls \n\n\\author Richard Beare. Department of Medicine, Monash University, Melbourne, Australia.",
   "itk_module" : "ITKReview",
   "itk_group" : "ITKReview",
   "in_place" : false

Code/BasicFilters/json/AreaOpeningImageFilter.json

@@ -50,7 +50,7 @@
     }
   ],
   "briefdescription" : "Morphological opening by attributes.",
-  "detaileddescription" : "An attribute opening removes blobs according to criteria such as area. When applied to grayscale images they have the effect of trimming peaks (regions brighter than their surroundings) based on area while leaving the rest of the image unchanged.\n\nThis code was contributed in the Insight Journal paper\n\n\"Grayscale morphological attribute operations\" by Beare R. https://www.insight-journal.org/browse/publication/203 \n\n\\author Richard Beare. Department of Medicine, Monash University, Melbourne, Australia.",
+  "detaileddescription" : "An attribute opening removes blobs according to criteria such as area. When applied to grayscale images they have the effect of trimming peaks (regions brighter than their surroundings) based on area while leaving the rest of the image unchanged.\n\nThis code was contributed in the Insight Journal paper\n\n\"Grayscale morphological attribute operations\" by Beare R. https://doi.org/10.54294/ifvjls \n\n\\author Richard Beare. Department of Medicine, Monash University, Melbourne, Australia.",
   "itk_module" : "ITKReview",
   "itk_group" : "ITKReview",
   "in_place" : false

Code/BasicFilters/json/BinShrinkImageFilter.json

@@ -47,7 +47,7 @@
     }
   ],
   "briefdescription" : "Reduce the size of an image by an integer factor in each dimension while performing averaging of an input neighborhood.",
-  "detaileddescription" : "The output image size in each dimension is given by:\n\noutputSize[j] = max( std::floor(inputSize[j]/shrinkFactor[j]), 1 );\n\nThe algorithm implemented can be describe with the following equation for 2D: \\f[ \\mathsf{I}_{out}(x_o,x_1) = \\frac{\\sum_{i=0}^{f_0}\\sum_{j=0}^{f_1}\\mathsf{I}_{in}(f_0 x_o+i,f_1 x_1+j)}{f_0 f_1} \\f] \n\nThis filter is implemented so that the starting extent of the first pixel of the output matches that of the input.\n\nThe change in image geometry from a 5x5 image binned by a factor of 2x2.\n\n\nThis code was contributed in the Insight Journal paper: \"BinShrink: A multi-resolution filter with cache efficient averaging\" by Lowekamp B., Chen D. https://www.insight-journal.org/browse/publication/912",
+  "detaileddescription" : "The output image size in each dimension is given by:\n\noutputSize[j] = max( std::floor(inputSize[j]/shrinkFactor[j]), 1 );\n\nThe algorithm implemented can be describe with the following equation for 2D: \\f[ \\mathsf{I}_{out}(x_o,x_1) = \\frac{\\sum_{i=0}^{f_0}\\sum_{j=0}^{f_1}\\mathsf{I}_{in}(f_0 x_o+i,f_1 x_1+j)}{f_0 f_1} \\f] \n\nThis filter is implemented so that the starting extent of the first pixel of the output matches that of the input.\n\nThe change in image geometry from a 5x5 image binned by a factor of 2x2.\n\n\nThis code was contributed in the Insight Journal paper: \"BinShrink: A multi-resolution filter with cache efficient averaging\" by Lowekamp B., Chen D. https://doi.org/10.54294/p39qox",
   "itk_module" : "ITKImageGrid",
   "itk_group" : "ImageGrid",
   "in_place" : false

Code/BasicFilters/json/BinaryClosingByReconstructionImageFilter.json

@@ -88,7 +88,7 @@
     }
   ],
   "briefdescription" : "binary closing by reconstruction of an image.",
-  "detaileddescription" : "This filter removes small (i.e., smaller than the structuring element) holes in the image. It is defined as: Closing(f) = ReconstructionByErosion(Dilation(f)).\n\nThe structuring element is assumed to be composed of binary values (zero or one). Only elements of the structuring element having values > 0 are candidates for affecting the center pixel.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://www.insight-journal.org/browse/publication/176 \n\n\\see MorphologyImageFilter , ClosingByReconstructionImageFilter , BinaryOpeningByReconstructionImageFilter",
+  "detaileddescription" : "This filter removes small (i.e., smaller than the structuring element) holes in the image. It is defined as: Closing(f) = ReconstructionByErosion(Dilation(f)).\n\nThe structuring element is assumed to be composed of binary values (zero or one). Only elements of the structuring element having values > 0 are candidates for affecting the center pixel.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://doi.org/10.54294/q6auw4 \n\n\\see MorphologyImageFilter , ClosingByReconstructionImageFilter , BinaryOpeningByReconstructionImageFilter",
   "itk_module" : "ITKBinaryMathematicalMorphology",
   "itk_group" : "BinaryMathematicalMorphology",
   "in_place" : false

Code/BasicFilters/json/BinaryContourImageFilter.json

@@ -75,7 +75,7 @@
     }
   ],
   "briefdescription" : "Labels the pixels on the border of the objects in a binary image.",
-  "detaileddescription" : "BinaryContourImageFilter takes a binary image as input, where the pixels in the objects are the pixels with a value equal to ForegroundValue. Only the pixels on the contours of the objects are kept. The pixels not on the border are changed to BackgroundValue.\n\nThe connectivity can be changed to minimum or maximum connectivity with SetFullyConnected() . Full connectivity produces thicker contours.\n\nhttps://www.insight-journal.org/browse/publication/217 \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see LabelContourImageFilter BinaryErodeImageFilter SimpleContourExtractorImageFilter",
+  "detaileddescription" : "BinaryContourImageFilter takes a binary image as input, where the pixels in the objects are the pixels with a value equal to ForegroundValue. Only the pixels on the contours of the objects are kept. The pixels not on the border are changed to BackgroundValue.\n\nThe connectivity can be changed to minimum or maximum connectivity with SetFullyConnected() . Full connectivity produces thicker contours.\n\nhttps://doi.org/10.54294/c7d3gv \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see LabelContourImageFilter BinaryErodeImageFilter SimpleContourExtractorImageFilter",
   "itk_module" : "ITKImageLabel",
   "itk_group" : "ImageLabel",
   "in_place" : true

Code/BasicFilters/json/BinaryFillholeImageFilter.json

@@ -66,7 +66,7 @@
     }
   ],
   "briefdescription" : "Remove holes not connected to the boundary of the image.",
-  "detaileddescription" : "BinaryFillholeImageFilter fills holes in a binary image.\n\nGeodesic morphology and the Fillhole algorithm is described in Chapter 6 of Pierre Soille's book \"Morphological Image Analysis:\nPrinciples and Applications\", Second Edition, Springer, 2003.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://www.insight-journal.org/browse/publication/176 \n\n\\see GrayscaleFillholeImageFilter",
+  "detaileddescription" : "BinaryFillholeImageFilter fills holes in a binary image.\n\nGeodesic morphology and the Fillhole algorithm is described in Chapter 6 of Pierre Soille's book \"Morphological Image Analysis:\nPrinciples and Applications\", Second Edition, Springer, 2003.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://doi.org/10.54294/q6auw4 \n\n\\see GrayscaleFillholeImageFilter",
   "itk_module" : "ITKLabelMap",
   "itk_group" : "LabelMap",
   "in_place" : false

Code/BasicFilters/json/BinaryGrindPeakImageFilter.json

@@ -76,7 +76,7 @@
     }
   ],
   "briefdescription" : "Remove the objects not connected to the boundary of the image.",
-  "detaileddescription" : "BinaryGrindPeakImageFilter grinds peaks in a grayscale image.\n\nGeodesic morphology and the grind peak algorithm is described in Chapter 6 of Pierre Soille's book \"Morphological Image Analysis:\nPrinciples and Applications\", Second Edition, Springer, 2003.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://www.insight-journal.org/browse/publication/176 \n\n\\see GrayscaleGrindPeakImageFilter",
+  "detaileddescription" : "BinaryGrindPeakImageFilter grinds peaks in a grayscale image.\n\nGeodesic morphology and the grind peak algorithm is described in Chapter 6 of Pierre Soille's book \"Morphological Image Analysis:\nPrinciples and Applications\", Second Edition, Springer, 2003.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://doi.org/10.54294/q6auw4 \n\n\\see GrayscaleGrindPeakImageFilter",
   "itk_module" : "ITKLabelMap",
   "itk_group" : "LabelMap",
   "in_place" : false

Code/BasicFilters/json/BinaryImageToLabelMapFilter.json

@@ -95,7 +95,7 @@
     }
   ],
   "briefdescription" : "Label the connected components in a binary image and produce a collection of label objects.",
-  "detaileddescription" : "BinaryImageToLabelMapFilter labels the objects in a binary image. Each distinct object is assigned a unique label. The final object labels start with 1 and are consecutive. Objects that are reached earlier by a raster order scan have a lower label.\n\nThe GetOutput() function of this class returns an itk::LabelMap .\n\nThis implementation was taken from the Insight Journal paper: https://www.insight-journal.org/browse/publication/176 \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see ConnectedComponentImageFilter , LabelImageToLabelMapFilter , LabelMap , LabelObject",
+  "detaileddescription" : "BinaryImageToLabelMapFilter labels the objects in a binary image. Each distinct object is assigned a unique label. The final object labels start with 1 and are consecutive. Objects that are reached earlier by a raster order scan have a lower label.\n\nThe GetOutput() function of this class returns an itk::LabelMap .\n\nThis implementation was taken from the Insight Journal paper: https://doi.org/10.54294/q6auw4 \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see ConnectedComponentImageFilter , LabelImageToLabelMapFilter , LabelMap , LabelObject",
   "itk_module" : "ITKLabelMap",
   "itk_group" : "LabelMap",
   "in_place" : false

Code/BasicFilters/json/BinaryMorphologicalClosingImageFilter.json

@@ -120,7 +120,7 @@
     }
   ],
   "briefdescription" : "binary morphological closing of an image.",
-  "detaileddescription" : "This filter removes small (i.e., smaller than the structuring element) holes and tube like structures in the interior or at the boundaries of the image. The morphological closing of an image \"f\" is defined as: Closing(f) = Erosion(Dilation(f)).\n\nThe structuring element is assumed to be composed of binary values (zero or one). Only elements of the structuring element having values > 0 are candidates for affecting the center pixel.\n\nThis code was contributed in the Insight Journal paper: \"Binary morphological closing and opening image filters\" by Lehmann G. https://www.insight-journal.org/browse/publication/58 \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleErodeImageFilter",
+  "detaileddescription" : "This filter removes small (i.e., smaller than the structuring element) holes and tube like structures in the interior or at the boundaries of the image. The morphological closing of an image \"f\" is defined as: Closing(f) = Erosion(Dilation(f)).\n\nThe structuring element is assumed to be composed of binary values (zero or one). Only elements of the structuring element having values > 0 are candidates for affecting the center pixel.\n\nThis code was contributed in the Insight Journal paper: \"Binary morphological closing and opening image filters\" by Lehmann G. https://doi.org/10.54294/bcwtvq \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleErodeImageFilter",
   "itk_module" : "ITKBinaryMathematicalMorphology",
   "itk_group" : "BinaryMathematicalMorphology",
   "in_place" : false

Code/BasicFilters/json/BinaryMorphologicalOpeningImageFilter.json

@@ -88,7 +88,7 @@
     }
   ],
   "briefdescription" : "binary morphological opening of an image.",
-  "detaileddescription" : "This filter removes small (i.e., smaller than the structuring element) structures in the interior or at the boundaries of the image. The morphological opening of an image \"f\" is defined as: Opening(f) = Dilatation(Erosion(f)).\n\nThe structuring element is assumed to be composed of binary values (zero or one). Only elements of the structuring element having values > 0 are candidates for affecting the center pixel.\n\nThis code was contributed in the Insight Journal paper: \"Binary morphological closing and opening image filters\" by Lehmann G. https://www.insight-journal.org/browse/publication/58 \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleErodeImageFilter",
+  "detaileddescription" : "This filter removes small (i.e., smaller than the structuring element) structures in the interior or at the boundaries of the image. The morphological opening of an image \"f\" is defined as: Opening(f) = Dilatation(Erosion(f)).\n\nThe structuring element is assumed to be composed of binary values (zero or one). Only elements of the structuring element having values > 0 are candidates for affecting the center pixel.\n\nThis code was contributed in the Insight Journal paper: \"Binary morphological closing and opening image filters\" by Lehmann G. https://doi.org/10.54294/bcwtvq \n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\n\\see MorphologyImageFilter , GrayscaleDilateImageFilter , GrayscaleErodeImageFilter",
   "itk_module" : "ITKBinaryMathematicalMorphology",
   "itk_group" : "BinaryMathematicalMorphology",
   "in_place" : false

Code/BasicFilters/json/BinaryNotImageFilter.json

@@ -54,7 +54,7 @@
     }
   ],
   "briefdescription" : "Implements the BinaryNot logical operator pixel-wise between two images.",
-  "detaileddescription" : "This class is parameterized over the types of the two input images and the type of the output image. Numeric conversions (castings) are done by the C++ defaults.\n\nThe total operation over one pixel will be\n\noutput_pixel = static_cast<PixelType>( input1_pixel != input2_pixel )\n\nWhere \"!=\" is the equality operator in C++.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://www.insight-journal.org/browse/publication/176",
+  "detaileddescription" : "This class is parameterized over the types of the two input images and the type of the output image. Numeric conversions (castings) are done by the C++ defaults.\n\nThe total operation over one pixel will be\n\noutput_pixel = static_cast<PixelType>( input1_pixel != input2_pixel )\n\nWhere \"!=\" is the equality operator in C++.\n\n\\author Gaetan Lehmann. Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France.\n\n\nThis implementation was taken from the Insight Journal paper: https://doi.org/10.54294/q6auw4",
   "itk_module" : "ITKLabelMap",
   "itk_group" : "LabelMap",
   "in_place" : true