Conference Paper

Level Set Segmentation of Cellular Images Based on Topological Dependence

DOI: 10.1007/978-3-540-89639-5_52 Conference: Advances in Visual Computing, 4th International Symposium, ISVC 2008, Las Vegas, NV, USA, December 1-3, 2008. Proceedings, Part I
Source: DBLP


Segmentation of cellular images presents a challenging task for computer vision, especially when the cells of irregular shapes
clump together. Level set methods can segment cells with irregular shapes when signal-to-noise ratio is low, however they
could not effectively segment cells that are clumping together. We perform topological analysis on the zero level sets to
enable effective segmentation of clumped cells. Geometrical shapes and intensities are important information for segmentation
of cells. We assimilated them in our approach and hence we are able to gain from the advantages of level sets while circumventing
its shortcoming. Validation on a data set of 4916 neural cells shows that our method is 93.3 ±0.6% accurate.

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Available from: Weimiao Yu, May 26, 2014
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    • "Timings for all figures are presented in appendix B. In our future work, we would like to extend our statistical physics approach to include topological dependence [31]–[33] and the subspace Mumford–Shah model [9]–[11]. Eden clustering can be used to include a topological dependence constraint developed recently to segment crowded objects [31] [32]. "
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