基于边界剥离的细胞图象分离算法

细胞图象的自动判读中，经常遇到聚堆细胞的问题，因而需要采用一种有效的分离算法把它们分离为单个细胞，目前存在的大多数分离算法都要求聚堆细胞连接处的凹陷性比较明显，或者要求在细胞连接处存在局部最小灰度值，否则难以实现正确分离，为了解决这一问题，提出了一种基于边界剥离的分离算法，该算法首先对聚堆细胞区域进行层层剥离，然后根据剥离结果判断是否发生了细胞分裂，进而完成分离，这样就避免了对凹陷性及局部最小灰度值的要求，实验结果表明算法是有效的。

Separating Algorithm for Cell Image Based on Boundary-Stripped

The clustering phenomenon often appears in the cell image auto reading system, i.e., some cells overlap or touch together to form a big area. It is necessary to design an effective algorithm to separate the clustering cells into single ones. Most of the existed separating algorithms are based on two hypotheses: 1) at points where cells touch, the cluster boundary tends to form an acute angle; 2) at points where the cells touch, the optical density is relatively low, otherwise it will be very difficult to separate them properly. But in the actual cell images, the two hypotheses are very difficult to be met. To solve this problem, an algorithm based on boundary stripped is presented in the paper. The boundary of clustering cells is stripped layer by layer in the algorithm. During stripping, judgement is made to determine whether the splitting has happened, and then the actual separation is taken in the original cell image. It does not require the clustering cells should necessarily meet the two hypotheses. The experiment result shows that the algorithm is effective.