  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 cell datasets 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.
