A bottleneck detection approach for separating overlapped cells. (15th September 2022) - Wang, Zhenzhou
Highlights: We propose a new bottleneck detection method to separate overlapped cells. The bottleneck points are detected by slope difference distribution. The largest concave parts are used to select the bottleneck points for overlapped cell separation. The proposed approach is significantly more robust than state of the art methods. Abstract: To separate the overlapped cells, a bottleneck detection approach is proposed in this paper. The cell image is segmented by slope difference distribution (SDD) threshold selection. For each segmented binary clump, its one-dimensional boundary is computed as the distance distribution between its centroid and each point on the two-dimensional boundary. The bottleneck points of the one-dimensional boundary is detected by SDD and then transformed back into two-dimensional bottleneck points. Two largest concave parts of the binary clump are used to select the valid bottleneck points. Two bottleneck points from different concave parts with the minimum Euclidean distance is connected to separate the binary clump with minimum-cut. The binary clumps are separated iteratively until the number of computed concave parts is smaller than two. We use four types of open-accessible cells to verify the effectiveness of the proposed approach and experimental results showed that the proposed approach is significantly more robust than state of the art methods.