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To separate the overlapped cells, a bottleneck detection approach is proposedin this paper. The cell image is segmented by slope difference distribution(SDD) threshold selection. For each segmented binary clump, its one-dimensionalboundary is computed as the distance distribution between its centroid and eachpoint on the two-dimensional boundary. The bottleneck points of theone-dimensional boundary is detected by SDD and then transformed back intotwo-dimensional bottleneck points. Two largest concave parts of the binaryclump are used to select the valid bottleneck points. Two bottleneck pointsfrom different concave parts with the minimum Euclidean distance is connectedto separate the binary clump with minimum-cut. The binary clumps are separatediteratively until the number of computed concave parts is smaller than two. Weuse four types of open-accessible cell datasets to verify the effectiveness ofthe proposed approach and experimental results showed that the proposedapproach is significantly more robust than state of the art methods.

Bottleneck detection by slope difference distribution: a robust approach for separating overlapped cells