/**
  \defgroup DataRepresentation Data Representation Objects

  ITK includes several data representation objects such as
  itk::Image, itk::Mesh, itk::Point, etc.
*/

/**
  \defgroup ImageObjects Image Representation Objects
  \ingroup DataRepresentation

  Objects required to represent images in ITK.
*/

/**
  \defgroup MeshObjects Mesh Representation Objects
  \ingroup DataRepresentation

  Objects required to represent meshes in ITK.
*/

/**
  \defgroup PathObjects Path Representation Objects
  \ingroup DataRepresentation

  Objects required to represent paths in ITK.
*/

/**
  \defgroup Geometry Geometry Representation Objects
  \ingroup DataRepresentation

  Objects required to represent geometrical entities like positions, vectors
  and space mappings.

  A detailed description of the rationale for these classes can be found in
  \ref GeometryPage.
*/

/**
  \defgroup DataAccess Data Access Objects

  ITK includes several ways to access data through the user of
  iterators, pointers, indexes, etc.
*/

/**
  \defgroup TensorObjects Tensor Image Objects

  Objects required for representing diffusion tensor images in ITK.
*/

/**
  \defgroup ImageAccess Image Access Objects
  \ingroup DataAccess
*/

/**
  \defgroup MeshAccess Mesh Access Objects
  \ingroup DataAccess
*/

/**
  \defgroup Iterators Iterators
  \ingroup DataAccess

  Iterators are the mechanism used to walk over the content of a particular
  data object. They allow to define paths and directions along which the data
  should be walked through.
*/

/**
  \defgroup ImageIterators Image Iterators
  \ingroup Iterators

  Image iterators allow to go through the content of an image in a predefined
  way. For a detailed description of the iterators' rationale see
  \ref ImageIteratorsPage.
*/

/**
  \defgroup DataProcessing Data Processing Objects

  ITK includes several ways to process the data using objects such as adaptors,
  functions, filters, and transforms.
*/

/**
  \defgroup Filters Filters
  \ingroup DataProcessing

  Filters implementing the operations on the pipeline architecture.
*/

/**
  \defgroup ImageFilters Image Filters
  \ingroup Filters

  Image filters process input images and produce output images. Inputs are
  unmodified. The pipeline architecture makes provisions for supporting
  streaming by using packets of data defined by regions.

  \sa itk::Image
  \sa itk::ImageRegion
*/

/**
  \defgroup MeshFilters Mesh Filters
  \ingroup Filters

  Mesh filters process input meshes and produce output meshes. Inputs are
  unmodified.

  \sa itk::Mesh
*/

/**
  \defgroup QEMeshModifierFunctions QuadEdge Mesh Modifier Functions
  \ingroup MeshFilters

  QuadEdge mesh modifier functions implement small, targeted modifications to
  QuadEdge meshes such as Euler operators (e.g., join/split vertex, flip edge).

  \sa itk::QuadEdgeMesh
*/

/**
  \defgroup IntensityImageFilters Intensity Image Filters
  \ingroup ImageFilters

  Intensity image filters only alter the values stored in image pixels.

  \sa itk::Image
  \sa itk::ImageRegion
*/

/**
  \defgroup MathematicalMorphologyImageFilters Mathematical Morphology Image Filters
  \ingroup IntensityImageFilters

  Mathematical morphology filters are a particular class of cellular automata.
  They modify the value of a pixel based on the values of a neighborhood.
  The neighborhood is now as the structured element.

  \sa itk::Image
  \sa itk::ImageRegion
  \sa itk::BinaryMorphologyImageFilter
*/


/**
  \defgroup ImageEnhancement Image Enhancement Filters
  \ingroup ImageFilters

  Image enhancement filters process an image to enhance the appearance
  of an image either for visualization purposes or for further processing.
  Examples of image enhancement filters available in ITK are: anisotropic diffusion,
  Gaussian filter, and histogram equalization.
*/

/**
  \defgroup ImageFeatureExtraction Image Feature Extraction Filters
  \ingroup ImageFilters

  Image feature extraction filters process an image to extract features of interest
  such as gradients, edges, distances, etc.

  Examples of image feature extraction algorithms available in ITK are: image gradients,
  first and second derivatives, and Danielson distance.
*/

/**
  \defgroup GradientFilters Image Gradient Filters
  \ingroup ImageFeatureExtraction

  These filters compute local gradients of images.
*/


/**
  \defgroup GeometricTransform Geometric Transformation Filters
  \ingroup Filters

  Geometric transformation Filters transform the coordinates of an image in
  various ways.

  Examples of geometric transformation Filters available in ITK are: image
  shrinking, and affine transformation.
*/

/**
  \defgroup PyramidImageFilter Image Pyramid Filters
  \ingroup GeometricTransform

  Image pyramid filters generate a set of downsampled versions of an image
  according to a user defined multi-resolution schedule.
*/

/**
  \defgroup ImageSegmentation Image Segmentation Filters
  \ingroup ImageFilters

  Image segmentation filters process an image to partition it into meaningful
  regions. Various types of image segmentation algorithms are available in ITK,
  such as unsupervised pixel classification methods, region-growing methods,
  watershed-based methods, deformable-model based methods, and level-set based
  methods.
*/

/**
  \defgroup IntensityImageSegmentation Intensity-Based Image Segmentation Filters
  \ingroup ImageSegmentation

  Intensity based image segmentation filters use intensity values of image
  pixels to segment an image. Typically, spatial contiguity is not considered
  in intensity-based segmentation filters.

  Examples of intensity-based algorithms in ITK are supervised and unsupervised
  pixel classification algorithms.
*/

/**
  \defgroup ClassificationFilters Pixel Classification Filters
  \ingroup IntensityImageSegmentation

  Pixel classification filters use statistical classification algorithms to
  assign a label to a given image pixel. Classification algorithms can be
  supervised when training data is available or unsupervised when no training
  data is available.
*/

/**
  \defgroup SupervisedClassificationFilters Supervised Classification Filters
  \ingroup ClassificationFilters

  Supervised classification filters rely on the existence of training data to
  classify pixels into different types. An example of supervised
  classification algorithm in ITK is the Gaussian classifier that uses the
  training data to build Gaussian models of intensity distributions.
*/

/**
  \defgroup UnSupervisedClassificationFilters Unsupervised Classification Filters
  \ingroup ClassificationFilters

  Unsupervised classification filters typically cluster the image intensity
  data into different groups.

  An example of unsupervised classification algorithm in ITK is the K-Means
  clustering algorithm.
*/

/**
  \defgroup WatershedSegmentation Watershed-based Segmentation Filters
  \ingroup IntensityImageSegmentation

  These filters segment an image based on intensity values using the watershed
  algorithm.
*/

/**
  \defgroup RegionBasedSegmentation Region-Based Segmentation Filters
  \ingroup ImageSegmentation

  These filters segment an image based on similarity of intensity values
  between spatially adjacent pixels. Examples of region-based segmentation
  filters in ITK include fuzzy connectedness, region growing, and Markov
  Random Fields (MRF).
*/

/**
  \defgroup FuzzyConnectednessSegmentation Fuzzy Connectedness-based Segmentation Filters
  \ingroup RegionBasedSegmentation

  These filters segment an image based on fuzzy connectedness principles.
  These methods typically start with one or more seed points and grow regions
  around these seed points based on fuzzy affinity.
*/

/**
  \defgroup RegionGrowingSegmentation Region Growing Filters
  \ingroup RegionBasedSegmentation

  Typically region growing involves starting several small regions on an image
  and merging them iteratively based on some pixel intensity similarity
  criterion. ITK provides an intensity and edge-based region-growing algorithm
  for segmentation.
*/

/**
  \defgroup MRFFilters Markov Random Field-based Filters
  \ingroup RegionBasedSegmentation

  Markov Random Field (MRF)-based Filters assume that the segmented image is
  Markovian in nature, i.e., adjacent pixels are likely to be of the same
  class. These methods typically combine intensity-based filters with MRF
  prior models also known as Gibbs prior models.
*/

/**
  \defgroup ModelImageSegmentation Model-Based Image Segmentation Filters
  \ingroup ImageSegmentation

  These filters segment an image by starting with a model and then updating
  the model based on image features and the updates are typically
  constrained by a-priori knowledge about the models.

  Examples of these types of algorithms in ITK include: deformable model
  (snakes)-based algorithms and level set-based algorithms.
*/

/**
  \defgroup MeshSegmentation Mesh Segmentation Filters
  \ingroup ModelImageSegmentation

  These algorithms represent models using a mesh and update the models based
  on image features.

  Examples of this type of filter in ITK include: balloon force filter and the
  deformable mesh filter.
*/

/**
  \defgroup LevelSetSegmentation Level Set-Based Segmentation Filters
  \ingroup ModelImageSegmentation

  These algorithms represent models implicitly using level-sets and update the
  models based on image features.

  Examples of these types of segmentation methods in ITK include: curvature
  flow-based filters, fast marching filters, and shape-detection filters.
*/

/**
  \defgroup HybridSegmentation Hybrid Segmentation Filters
  \ingroup ImageSegmentation

  These filters are hybrid between intensity-based, region-based, or
  model-based image segmentation filters.
*/

/**
  \defgroup RegistrationFilters Registration Filters
  \ingroup Filters
*/

/**
  \defgroup RegistrationComponents Components of Registration Methods
  \ingroup RegistrationFilters

  Registration methods are implemented by combining basic components. This
  framework allows great flexibility in the construction of registration
  methods, and maximizes code reuse. The basic components of a registration
  method are described in \ref RegistrationPage.
*/

/**
  \defgroup RegistrationMetrics Similarity Metrics of Registration Methods
  \ingroup RegistrationComponents

  Similarity metrics are the objects that evaluate how similar two objects
  are. They are used in registration to quantify how well a transform is
  mapping the reference object on top of the target object.
*/

/**
  \defgroup ImageRegistration Image Registration Methods
  \ingroup RegistrationFilters
*/

/**
  \defgroup RigidImageRegistration Rigid Registration Methods
  \ingroup ImageRegistration
*/

/**
  \defgroup AffineImageRegistration Affine Registration Methods
  \ingroup ImageRegistration
*/

/**
  \defgroup DeformableImageRegistration Deformable Registration Methods
  \ingroup ImageRegistration
*/

/**
  \defgroup ModelImageRegistration Model - Image Registration Methods
  \ingroup RegistrationFilters
*/

/**
  \defgroup PointSetToImageRegistration PointSet to Image Registration Methods
  \ingroup ModelImageRegistration
*/

/**
  \defgroup IOFilters Input and Output Filters
  \ingroup Filters
*/

/**
  \defgroup DataSources Data Sources
  \ingroup DataProcessing
*/

/**
  \defgroup ImageAdaptors Image Adaptors
  \ingroup DataProcessing

  Image adaptors are an implementation of the <em>Adaptor Design Pattern</em>.
  They are designed to present an image of a particular type as being an image
  of a different type. Adaptors perform simple operations on pixel values for
  which is not easy to justify the use of an image filter.

  One of the principal tasks of Image adaptors is to perform casting.

  For example: you have an image whose pixels are of type
  <tt>unsigned char</tt> and you need to feed this image in a process that
  expects pixels of type <tt>double</tt>. You have the option of using an
  image filter (see \ref ImageFilters) that converts the
  <tt>unsigned char</tt> input image into another of <tt>double</tt> pixel
  type. However this filter will allocate memory for this second image and
  will need to be executed. Image adaptors allow to simulate that you have
  made the conversion but will avoid the overhead in memory. There is however
  a penalty in performance.

  The mechanism used by image adaptors is to provide a simple function that
  will be used by image iterators (see \ref ImageIteratorsPage) to convert the
  value of a pixel, in a pixel-by-pixel basis.
*/

/**
  \defgroup Functions Functions
  \ingroup DataProcessing

  Functions providing an efficient mechanism for computing values.
*/

/**
  \defgroup ImageFunctions Image Functions
  \ingroup Functions

  Image functions compute single values using data from a previously specified
  image. A value can be computed at an image index, continuous index or point.

  \sa itk::Image
  \sa itk::Index
  \sa itk::ImageFunction
*/

/**
  \defgroup ImageInterpolators Image Interpolators
  \ingroup ImageFunctions

  Image interpolators compute pixel values in non-grid positions. A value can
  be computed at an image index, continuous index or point.

  \sa itk::Image
  \sa itk::ImageFunction
*/

/**
  \defgroup SpatialFunctions Spatial Functions
  \ingroup Functions
*/

/**
  \defgroup FiniteDifferenceFunctions Finite Difference Functions
  \ingroup Functions

  Finite difference functions are used in the finite difference solver (FDS)
  framework for solving partial differential equations on images using an
  iterative, finite difference update scheme.

  \sa itk::FiniteDifferenceImageFilter
  \sa itk::FiniteDifferenceFunction
*/

/**
  \defgroup Operators Operators
  \ingroup DataProcessing

  Operators implement the abstraction of performing an operation using data
  from a neighborhood of a pixel. ITK Operators work in conjunction with
  neighborhood iterators in order to walk over an image.
*/

/**
  \defgroup Numerics Numerics

  ITK provides support for numerical operations at two levels. First, ITK uses
  an external library called VNL, which is one component of the VXL toolkit.
  This library provides linear algebra, optimization, and FFTs. Second, ITK
  provides numerical optimizers designed for the registration framework and
  statistical classes designed to be used for classification and segmentation.
 */

/**
  \defgroup Optimizers Optimizers
  \ingroup Numerics RegistrationComponents

  Set of Optimization methods. Some of these methods are adaptors for
  classes already defined in the VNL library. These methods have been
  particularly tailored to be used as modular components in the
  registration framework.

  \sa RegistrationComponents
 */

/**
  \defgroup SystemObjects System Objects
*/

/**
  \defgroup ITKSystemObjects ITK System Objects
  \ingroup SystemObjects

  Basic system objects used in building ITK.
*/

/**
  \defgroup OSSystemObjects OS System Objects
  \ingroup SystemObjects

  Basic operating system related system objects in ITK.
*/

/**
  \defgroup LoggingObjects Logging Objects
  \ingroup OSSystemObjects

  Objects implementing a logging framework for reporting messages,
  warnings, and errors during ITK processing.
*/

/**
  \defgroup ThreadSafetyGroup Thread Safety

  Classes catalogued according to their compliance with thread safety.

  ITK is designed to be thread-safe, but in some particular cases this
  capability cannot be supported and for this reason a classification is
  needed.
*/

/**
  \defgroup ThreadSafe Thread Safe classes
  \ingroup ThreadSafetyGroup
*/

/**
  \defgroup ThreadUnSafe Thread Unsafe Classes
  \ingroup ThreadSafetyGroup
*/

/**
  \defgroup ThreadSafetyUnknown Thread Safety Unknown
  \ingroup ThreadSafetyGroup
*/

/**
  \defgroup MultiThreadingGroup MultiThreading Support

  Filters classified according to their support for performing processing in
  multiple threads.
*/

/**
  \defgroup MultiThreaded MultiThreaded Filters
  \ingroup MultiThreadingGroup

  Filters dividing the processing across several threads.
*/

/**
  \defgroup SingleThreaded  SingleThreaded Filters
  \ingroup MultiThreadingGroup

  Filters performing all its processing in a single thread.
*/


/**
  \defgroup ShouldBeThreaded Threading-friendly Filters
  \ingroup MultiThreadingGroup

  Filters that could benefit from a multi-threading implementation.
*/

/**
  \defgroup StreamingGroup Region-wise Processing Objects

  Filters processing images region by region. These filters may support
  streaming.
*/

/**
  \defgroup Streamed Streaming Filters
  \ingroup StreamingGroup

  Filters that can respond to a request for a portion of the output using
  only a portion of the input.
*/

/**
  \defgroup CannotBeStreamed Streaming-unfriendly Filters
  \ingroup StreamingGroup

  Filters that cannot be streamed. These filters require either all the input
  or produce all the output.
*/


/**
  \defgroup ShouldBeStreamed Streaming-friendly Filters
  \ingroup StreamingGroup

  Filters that can potentially be modified to support streaming but currently
  are not supporting it.
*/

/**
  \defgroup Deprecated Deprecated Classes

  Classes that are scheduled to be removed from the toolkit. Their
  functionality is now provided by other classes or changes in the toolkit have
  made them useless. Please report to their documentation and look in to their
  "Deprecated" section. This section should indicate what to do to replace
  this class in any affected code.
*/

/**
  \defgroup DataReadWrite Data Reading/Writing Objects

  Classes related to reading/writing data from/to disk.
*/

/**
  \defgroup WriterObjects Image Writing Objects
  \ingroup DataReadWrite

  Objects that allow writing to a file data produced by ITK filters.
*/
