vtkImageEuclideanDistance

Section: Visualization Toolkit Imaging Classes

Usage

vtkImageEuclideanDistance implements the Euclidean DT using Saito's algorithm. The distance map produced contains the square of the Euclidean distance values.

The algorithm has a o(n^(D+1)) complexity over nxnx...xn images in D dimensions. It is very efficient on relatively small images. Cuisenaire's algorithms should be used instead if n >> 500. These are not implemented yet.

For the special case of images where the slice-size is a multiple of 2^N with a large N (typically for 256x256 slices), Saito's algorithm encounters a lot of cache conflicts during the 3rd iteration which can slow it very significantly. In that case, one should use ::SetAlgorithmToSaitoCached() instead for better performance.

References:

T. Saito and J.I. Toriwaki. New algorithms for Euclidean distance transformations of an n-dimensional digitised picture with applications. Pattern Recognition, 27(11). pp. 1551--1565, 1994. O. Cuisenaire. Distance Transformation: fast algorithms and applications to medical image processing. PhD Thesis, Universite catholique de Louvain, October 1999. http://ltswww.epfl.ch/~cuisenai/papers/oc_thesis.pdf

To create an instance of class vtkImageEuclideanDistance, simply invoke its constructor as follows

  obj = vtkImageEuclideanDistance

Methods

The class vtkImageEuclideanDistance has several methods that can be used. They are listed below. Note that the documentation is translated automatically from the VTK sources, and may not be completely intelligible. When in doubt, consult the VTK website. In the methods listed below, obj is an instance of the vtkImageEuclideanDistance class.