Module ITKWatersheds

Collaboration diagram for Module ITKWatersheds:

Classes
class	itk::watershed::Boundary< TScalarType, TDimension >
class	itk::watershed::BoundaryResolver< TPixelType, TDimension >
class	itk::watershed::EquivalenceRelabeler< TScalarType, TImageDimension >
class	itk::IsolatedWatershedImageFilter< TInputImage, TOutputImage >
	Isolate watershed basins using two seeds. More...
class	itk::OneWayEquivalencyTable
	Hash table to manage integral label equivalencies that are order dependent. More...
class	itk::watershed::Relabeler< TScalarType, TImageDimension >
class	itk::watershed::Segmenter< TInputImage >
class	itk::watershed::SegmentTable< TScalarType >
class	itk::watershed::SegmentTree< TScalarType >
class	itk::watershed::SegmentTreeGenerator< TScalarType >
class	itk::TobogganImageFilter< TInputImage >
	toboggan image segmentation The Toboggan segmentation takes a gradient magnitude image as input and produces an (over-)segmentation of the image based on connecting each pixel to a local minimum of gradient. It is roughly equivalent to a watershed segmentation of the lowest level. More...
class	itk::WatershedImageFilter< TInputImage >
	A low-level image analysis algorithm that automatically produces a hierarchy of segmented, labeled images from a scalar-valued image input. More...
class	itk::WatershedMiniPipelineProgressCommand

Detailed Description

This module contains classes related to watershed segmentation. The watershed algorithm proceeds in several steps. First, an initial classification of all points into catchment basin regions is done by tracing each point down its path of steepest descent to a local minima. Next, neighboring regions and the boundaries between them are analyzed according to some saliency measure (such as minimum boundary height) to produce a tree of merges among adjacent regions. These merges occur at different maximum saliency values. Finally, a label images are generated from the merge tree.

Dependencies:

Classes

Detailed Description