Publication 2010 Issue No. 2 - February Abstract - Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D
Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D
February 2010 (vol. 32 no. 2)
pp. 321-334
 ASCII Text x Leo Grady, "Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 321-334, February, 2010.
 BibTex x @article{ 10.1109/TPAMI.2008.289,author = {Leo Grady},title = {Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {32},number = {2},issn = {0162-8828},year = {2010},pages = {321-334},doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.289},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - Minimal Surfaces Extend Shortest Path Segmentation Methods to 3DIS - 2SN - 0162-8828SP321EP334EPD - 321-334A1 - Leo Grady, PY - 2010KW - 3D image segmentationKW - minimal surfacesKW - shortest pathsKW - Dijkstra's algorithmKW - boundary operatorKW - total unimodularityKW - linear programmingKW - minimum-cost circulation network flow.VL - 32JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -
Leo Grady, Siemens Corporate Research, East Princeton
Shortest paths have been used to segment object boundaries with both continuous and discrete image models. Although these techniques are well defined in 2D, the character of the path as an object boundary is not preserved in 3D. An object boundary in three dimensions is a 2D surface. However, many different extensions of the shortest path techniques to 3D have been previously proposed in which the 3D object is segmented via a collection of shortest paths rather than a minimal surface, leading to a solution which bears an uncertain relationship to the true minimal surface. Specifically, there is no guarantee that a minimal path between points on two closed contours will lie on the minimal surface joining these contours. We observe that an elegant solution to the computation of a minimal surface on a cellular complex (e.g., a 3D lattice) was given by Sullivan [47]. Sullivan showed that the discrete minimal surface connecting one or more closed contours may be found efficiently by solving a Minimum-cost Circulation Network Flow (MCNF) problem. In this work, we detail why a minimal surface properly extends a shortest path (in the context of a boundary) to three dimensions, present Sullivan's solution to this minimal surface problem via an MCNF calculation, and demonstrate the use of these minimal surfaces on the segmentation of image data.

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Index Terms:
3D image segmentation, minimal surfaces, shortest paths, Dijkstra's algorithm, boundary operator, total unimodularity, linear programming, minimum-cost circulation network flow.
Citation:
Leo Grady, "Minimal Surfaces Extend Shortest Path Segmentation Methods to 3D," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 321-334, Feb. 2010, doi:10.1109/TPAMI.2008.289