Multi-channel Chan-Vese model for unsupervised segmentation of nuclei from breast histopathological images. (September 2021)

• Record Type: Journal Article
• Authors: Rashmi, R; Prasad, Keerthana; Udupa, Chethana Babu K.
• Is Part Of: Computers in biology and medicine. Volume 136(2021)
• Publication Date: 2021-09
• Subjects: Image segmentation -- Breast cancer -- Histopathology images -- H&E stains -- Unsupervised learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104651
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: T he pathologist determines the malignancy of a breast tumor by studying the histopathological images. In particular, the characteristics and distribution of nuclei contribute greatly to the decision process. Hence, the segmentation of nuclei constitutes a crucial task in the classification of breast histopathological images. Manual analysis of these images is subjective, tedious and susceptible to human error. Consequently, the development of computer-aided diagnostic systems for analysing these images have become a vital factor in the domain of medical imaging. However, the usage of medical image processing techniques to segment nuclei is challenging due to the diverse structure of the cells, poor staining process, the occurrence of artifacts, etc. Although supervised computer-aided systems for nuclei segmentation is popular, it is dependent on the availability of standard annotated datasets. In this regard, this work presents an unsupervised method based on Chan-Vese model to segment nuclei from breast histopathological images. The proposed model utilizes multi-channel color information to efficiently segment the nuclei. Also, this study proposes a pre-processing step to select appropriate color channel such that it discriminates nuclei from the background region. An extensive evaluation of the proposed model on two challenging datasets demonstrates its validity and effectiveness. Highlights: Selection of appropriate template image for color normalization of H&E stained images. Identification and selection of appropriate color channels for accurate segmentation of nuclei. Modified Chan-Vese model is used for unsupervised nuclei segmentation from breast histopathological images.