Automated Nuclei Segmentation Approach based on Mathematical Morphology for Cancer Scoring in Breast

 Automated Nuclei Segmentation Approach based on Mathematical Morphology for Cancer Scoring in Breast Tissue Images

Aymen Mouelhi1, Mounir Sayadi1, Farhat Fnaiech1 and Karima Mrad2

1Laboratory of Signal Image and Energy Mastery, ENSIT-University of Tunis, Tunisia.

2Morbid Anatomy Service, Salah Azaiez Institute of Oncology, Tunisia

Abstract: 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.

Keywords: Breast cancer, immunohistochemical image analysis, color deconvolution, morphological operators.

Received September 11, 2014; accepted March 23, 2015

Read 1738 times Last modified on Thursday, 07 January 2021 07:05
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