Implementation of weighted Dijkstra’s shortest-path algorithm for n-D images
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/1520
This paper describes the ITK implementation of a shortest path extraction algorithm based on graph representation of the image and the Dijkstra shortest path algorithm. The method requires the user to provide two inputs: 1. path information in the form of start, end, and neighboring mode, the form of which path is allowed to propagate between neighboring pixels, and 2. a weighting function which sets the distance metric between neighboring pixels. A number of perspectives for choosing weighting functions are given, as well as examples using real images. This paper can also serve as an example for utilizing the Boost C++ graph library into the ITK framework.
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Categories: Data Representation, Distance maps, Filtering, Neighborhood filters, Path
Keywords: minimal path, centerline, vessel segmentation, ITK, boost
Toolkits: ITK, CMake
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