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.

中文翻译：

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基于多尺度模糊聚类算法的宫颈细胞学图像核分割。

在宫颈癌细胞的筛选中，细胞图像中核的准确识别和分割是宫颈癌早期诊断的关键部分。重叠，染色不均，对比度差以及其他原因给宫颈核分割带来了挑战。我们提出了一种基于多尺度模糊聚类算法的子宫颈细胞核分割方法，该方法可以对子宫颈细胞团图像进行不同尺度的分割。在测量节点的兴趣度之前，我们采用基于面积的新颖的兴趣度。这两种方法的应用，不仅解决了聚类算法的分类号选择问题，而且大大提高了核识别性能。该方法由IBSI2014和IBSI2015公共数据集评估。