Automated nuclei segmentation approach based on mathematical morphology for cancer scoring in breast tissue images

In this work, we propose an automated approach able to perform accurate nuclear segmentation in immunohistochemical breast tissue images in order to provide quantitative evaluation of estrogen or progesterone receptor status that will help pathologists in their diagnosis. The presented method is based on color deconvolution and an enhanced morphological processing, which is used to identify positive stained nuclei and to separate all clustered nuclei in the microscopic image for a subsequent cancer scoring. Experiments on several breast cancer images of different patients admitted into the Tunisian Salah Azaiez cancer center, show the efficiency of the proposed method when compared to the manual evaluation of experts. On the whole image database, we recorded more than 97% for both accuracy of detected nuclei and cancer scoring over the truths provided by experienced pathologists.