21 July 2017 A novel unsupervised segmentation method for overlapping cervical cell images
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042028 (2017) https://doi.org/10.1117/12.2281649
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Overlapping cell segmentation is a prerequisite for the analysis of cervical smear images. Because of the complexity of overlapping situations and the poor contrast of overlapping edges, this problem is one of the most challenges in this field. In this paper, a novel unsupervised segmentation method without needing the training data for overlapping cervical smear images is proposed. First, this method uses a kind of graph cuts to separate all cell clumps from the background. A cell clump may contain the different number of cervical cells. Second, each clump is segmented into non-overlapping regions as rough cells using Voronoi diagram. Third, in order to refine the segmentation of overlapping regions, a minimum enclosing ellipse is used to fit in each rough cell and the overlapped parts of each cell are replaced with the relational regions in this ellipse. Finally, the above overlapped parts and the connected parts of the Voronoi rough cell are merged to form a complete cell. Experiments are conducted on 2 publicly released ISBI datasets and results show that the proposed segmentation method achieves the state-of-art performance.
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Lili Zhao, Jianping Yin, Yongkai Ye, Kuan Li, Minghui Qiu, "A novel unsupervised segmentation method for overlapping cervical cell images", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042028 (21 July 2017); doi: 10.1117/12.2281649; https://doi.org/10.1117/12.2281649
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