基于边缘链码信息的黏连细胞分割算法

Segmentation Algorithm for Adherent Cell Images Based on Edge Chain-Code Information

介绍了一种基于边缘链码信息的黏连细胞自动分割算法.该算法对弱对比度的细胞图像预处理;对二值化图像进行链码跟踪,并计算边缘各点的链码和、链码差、等效周长、弧弦比等特征参数;利用特征参数判断边缘光滑段、边缘角点;对真实分割角点进行线性插值最终实现黏连细胞的分割.将该算法应用于2组细胞图像序列共120帧图像的分割中,不仅解决了黏连细胞的分割难题,而且能够准确进行细胞凹陷的修补和细胞图像的简单计数.统计结果表明,相比于阈值法和先验模型法,该算法的分割成功帧占整个序列的百分比提高40%～60%.

A segmentation algorithm for adherent cell images based on edge chain-code information is presented in this paper. Firstly, a pre-processing was carried out for the cell image with low contrast ratio. Then edge chain code of cell binary image was used to keep track of their contours and extract their shape features, such as chain code sum, chain code diff, curvature radius and approximate perimeter. Further, the criterion of distinguishing edge inflexion and sleek curve section was provided. In the end, the adherent cell images were separated through linear interpolation between authentic corners. The presented algorithm has been applied to two sequence images with 120 frames. The segmentation results show that proposed algorithm can not only solve the problem of segmentation but also repair cell edge hollow as well as can count the number of cells successfully. Compared with the threshold algorithm and priori model algorithm, this algorithm can improve the success rate by 40%-60%.