Parallel N-Dimensional Exact Signed Euclidean Distance Transform

Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/307
The computation speed for distance transforms becomes important in a wide variety of image processing applications. Current ITK library filters do not see any benefit from a multithreading environment. We introduce a three-dimensional signed parallel implementation of the exact Euclidean distance transform algorithm developed by Maurer et al. with a theoretical complexity of O(n/p) for n voxels and p threads. Through this parallelization and efficient use of data structures we obtain approximately 3
times mean speedup on standard tests on a 4-processor machine compared with the current ITK exact Euclidean distance transform filter.
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minus Automatic Testing Results by Insight-Journal Dashboard on Thu Apr 26 00:56:49 2007 for revision #9
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plus Automatic Testing Results by Insight-Journal Dashboard on Thu Apr 26 00:47:59 2007 for revision #8
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plus Automatic Testing Results by Insight-Journal Dashboard on Thu Apr 26 00:39:45 2007 for revision #7
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plus Automatic Testing Results by Insight-Journal Dashboard on Thu Apr 26 00:22:15 2007 for revision #6
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plus Automatic Testing Results by Insight-Journal Dashboard on Thu Apr 26 00:20:03 2007 for revision #5
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plus Automatic Testing Results by Insight-Journal Dashboard on Fri Jan 19 16:15:30 2007 for revision #4
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plus Automatic Testing Results by Insight-Journal Dashboard on Mon Sep 25 04:34:15 2006 for revision #3
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plus Automatic Testing Results by Insight-Journal Dashboard on Mon Sep 25 01:06:40 2006 for revision #2
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plus The Need For Speed by Nick Tustison on 10-12-2006 for revision #3
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plus Promising contribution, but limited to dimension 3, and broken build by Gaetan Lehmann on 09-17-2006 for revision #1
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Categories: Programming, Programming
Keywords: Euclidean distance transform, parallel distance transform, distance transform
Toolkits: ITK, CMake
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