Self-adjusting nuclei segmentation (SANS) of Hematoxylin-Eosin stained histopathological breast cancer images
Yuxin Cui, Jianjun Hu · 2016
Hematoxylin-Eosin (H&E) stained breast cancer (BC) histopathological images are widely used in breast cancer diagnosis, for which segmenting the cells/nuclei from the images is a major operation. However, most of current methods are sensitive to the characteristics of the images. In this paper, we propose a self-adjusting method for adaptive cell/nuclei segmentation from HE BC images, which works on BC images of different types and quality. To deal with the diversity of nuclei sizes, our approach employs an ellipse detector to estimate nuclei size, which is essential for noise elimination and for addressing the cell overlap issue. Then we propose a novel seed detection method based on the topological skeleton model in the marker-controlled watershed transform framework. The experiment result shows that our method is effective and efficient, and outperforms the state-of-the-art approaches on various types of HE BC images.