Level Set Segmentation of Cellular Images Based on Topological Dependence

Authors:
Weimiao Yu;Hwee Kuan Lee;Srivats Hariharan;Wenyu Bu;Sohail Ahmed
Affiliations:
Bioinformatics Institute, #07-01, Matrix, Singapore 138671;Bioinformatics Institute, #07-01, Matrix, Singapore 138671;Institute of Medical Biology, #06-06, Immunos, Singapore 138648;Institute of Medical Biology, #06-06, Immunos, Singapore 138648;Institute of Medical Biology, #06-06, Immunos, Singapore 138648
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

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Abstract

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.