Mesh Segmentation Filters
[Model-Based Image Segmentation Filters]

Collaboration diagram for Mesh Segmentation Filters:

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Detailed Description

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.

Classes
class	itk::BalloonForceFilter< TInputMesh, TOutputMesh >
	BalloonForceFilter is used to apply balloon force and the potential force onto the 2D deformable model. For 3D (multi-slices) segmentation please use BalloonForce3DFilter The balloon force is vertical to the surface of the model. The potential force is given out by the estimated boundary points. These two will meet a balance at the boundary, thus the deformable model will fit to the boundary. Users should use deformable model as input using SetInput and also provide the filter with a potential image which will provide the estimated bounday. The image should be a binary image with object and background labelled differently. These image can be given by GibbsPriorFilter or any other segmentation filters. When the nodes on the model stopped at the estimated boundary, using the GradientFit method to fit the model using the gradient information in the original image. More...
class	DeformableMesh3D
	The DeformableMesh3DFilter is used to deform a mesh (deformable model) under a potential force in 2D or 3D. The potential force is derived from the gradient information in the medical image and it will make the model deform to fit to the boundary features. Inputs are: (1) A deformable triangular model (Mesh). Import using the SetInput method. (2) A gradient map that make the model deform to fit to the estimated boundary. the gradient should be based on the 2nd derive of the original image. So the nodes on the model surface will stop at the edge features in the original image. More...
class	itk::DeformableSimplexMesh3DFilter< TInputMesh, TOutputMesh >
	Three-dimensional deformable model for image segmentation. More...