TY - JOUR
T1 - Multi-channel Chan-Vese model for unsupervised segmentation of nuclei from breast histopathological images
AU - Rashmi, R.
AU - Prasad, Keerthana
AU - Udupa, Chethana Babu K.
N1 - Funding Information:
We thank Prof. Sudarsan N S Acharya for his inputs on the mathematical concepts required for this study. We also thank Prof. Aravinda Bhat for his inputs on the English language.
Publisher Copyright:
© 2021
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85111331466&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111331466&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2021.104651
DO - 10.1016/j.compbiomed.2021.104651
M3 - Article
AN - SCOPUS:85111331466
SN - 0010-4825
VL - 136
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 104651
ER -