| Automation Technology, Control Technology |
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| Automatic segmentation for cell images based on support vector machine and ellipse fitting |
| LIAO Miao, ZHAO Yu-qian, ZENG Ye-zhan, HUANG Zhong-chao, ZHANG Bing-kui, ZOU Bei-ji |
1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China;
3. Institute of Biomedical Engineering, Central South University, Changsha 410083, China;
4. Binnan Hospital of Shengli Petroleum Administration Bureau, Binzhou 256600, China |
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Abstract
An automatic segmentation method for microscopic cell images was presented with special focus on the problem of overlapping cell splitting. The binary operation was performed on original image, and multiple shape features of each connected region were extracted and tested with support vector machine (SVM) to distinguish single and overlapping cell regions. The overlapping cell region was separated by splitting point pair via bottleneck detection. The split edges were modified by an improved ellipse fitting method based on the circle or ellipse shape of cells. The new generated cell regions can effectively reflect the true shape of the overlapping cells. The above steps on the new generated regions were repeated until all overlapping cells were separated. The experimental results show that the proposed method can prevent both over-and under-segmentation more effectively and achieve higher accuracy, sensitivity and specificity for cell segmentation compared with many existing methods．
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Published: 25 April 2017
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Cite this article:
LIAO Miao, ZHAO Yu-qian, ZENG Ye-zhan, HUANG Zhong-chao, ZHANG Bing-kui, ZOU Bei-ji. Automatic segmentation for cell images based on support vector machine and ellipse fitting. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 722-728.
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基于支持向量机和椭圆拟合的细胞图像自动分割
提出细胞显微图像的自动分割方法,重点解决重叠细胞之间的分割难题.对原始图像进行二值化,提取每个连通区域的多个形状特征并应用支持向量机(SVM)进行测试,区分单个与重叠细胞.运用瓶颈检测寻找分离点对分割重叠细胞.基于细胞的椭圆或圆形结构,对分割后的边缘,应用改进的椭圆拟合法进行修正,修正后的细胞区域能够有效地反映重叠在一起的细胞的真实形状.对新生成的封闭区域进行循环检测,直至所有重叠细胞分割完毕.实验结果表明,相较于多种现有的细胞分割方法,采用该算法能够更有效地抑制过分割和欠分割,分割准确率、敏感度和特异度高.
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