Affiliations: College of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Haoyang Tang, College of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, China. E-mail: tanghaoyang@xupt.edu.cn.
Abstract: As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment adherent cells. Experimental results show that the proposed algorithm can achieve 93.01% accuracy, 93.93% sensitivity and 95.69% specificity. Compared with other segmentation algorithms of breast cell, the proposed algorithm can not only solve the difficulty of segmenting adherent cells, but also improve the segmentation accuracy of adherent cells.
