Implementation of weighted Dijkstra’s shortest-path algorithm for n-D images

Weizman, Lior1*,Freiman, Moti,Joskowicz, Leo
1.Hebrew University of Jerusalem
Abstract

Abstract

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.

Keywords

minimal pathITKboostvessel segmentationcenterline
Manuscript
Source Code and Data

Source Code and Data

CMakeLists.txt4.7 KBDatatest_2D.vtk17 KBtest_2D_result_hybrid_metric_full_neighbors.vtk8.7 KBtest_2D_result_hybrid_metric_non_full_neighbors.vtk8.7 KBtest_2D_result_simple_metric_full_neighbors.vtk8.7 KBtest_3D_result_hybrid_metric_full_neighbors.vtk53.2 KBtest_3D.vtk106 KBtest_3D_result_hybrid_metric_non_full_neighbors.vtk53.2 KBtest_3D_result_simple_metric_non_full_neighbors.vtk53.2 KBIJMacros.txt3.4 KBREADME.txt620 BsrcImageCompare.cxx8 KBitkDijkstraFilterTest2D.cpp3.4 KBitkDijkstraFilterTest3D.cpp3.5 KBitkShortestPathImageFilter.h3.3 KBitkShortestPathImageFilter.txx8.6 KBitkWeightGradAngleMetricCalculator.h3.4 KBitkWeightGradAngleMetricCalculator.txx3.3 KBitkWeightMetricCalculator.h2.9 KBitkWeightMetricCalculator.txx1.3 KBitkWeightSimpleMetricCalculator.h2.2 KBitkWeightSimpleMetricCalculator.txx1.6 KB

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