Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm

In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results.