基于水平集和凹点区域检测的粘连细胞分割方法

为解决具有灰度不均匀、低对比度以及边缘模糊等缺陷的粘连细胞分割问题，提出结合水平集方法的轮廓提取和凹点区域检测的细胞分割方法.利用水平集方法可很好地解决曲线演化过程中的拓扑变化问题，结合细胞图像的区域信息和边缘信息，能有效提取细胞的边缘轮廓.该方法可最大限度地保留细胞边缘轮廓的几何特征.根据多边形的凹凸性，循环迭代检测粘连细胞轮廓上的凹点区域，确定细胞的粘连位置，最后确定粘连位置的分割连接线.对几十幅不同粘连细胞图像的分割实验结果表明，该方法易于实现，鲁棒性强，效果明显，细胞分割的平均准确率达到83.01%，优于分水岭及聚类分割方法.

Overlapping Cell Segmentation Based on Level Set and Concave Area Detection

Overlapping cell images with inhomogeneity intensity, low contrast and edge blurring are diffi-cult to be segmented. The author proposes a new cell segmentation algorithm combining level set method and concave area detection. First, the level set method can easily handle topology changes of the evolving contour. And it can be employed to obtain the cell profile, combined with the regional information and edge information. This step keeps the geometric characteristics of the cell profile effectively. Second, the concave area of overlapping contour location based on the concave and convex of polygons was searched for. Finally, the splitting line of overlapping cells at the location of concave area was determined. Experi-ments on dozens of different overlapping cell images segmentation show that the algorithm is robust, effec-tive and easy to implement. The average accuracy of cell segmentation reaches to 83. 01%, which is su-perior to the results of the watershed and k-means clustering methods.