Automated segmentation and classification of nuclei in histopathological images
annotated; classifier; computer-assisted diagnosis; deep learning; digital image analysis; histopathological images; machine learning; nuclear; nuclei; segmentation; whole slide images
Various kinds of cancer are detected and diagnosed using histopathological analysis. Recent advances in whole slide scanner technology and the shift towards digitisation of whole slides have inspired the application of computational methods on histological data. Digital analysis of histopathological images has the potential to tackle issues accompanying conventional histological techniques, like the lack of objectivity and high variability. In this paper, we present a framework for the automated segmentation of nuclei from human histopathological whole slide images, and their classification using morphological and colour characteristics of the nuclei. The segmentation stage consists of two methods, thresholding and the watershed transform. The features of the segmented regions are recorded for the classification stage. Experimental results show that the knowledge from the selected features is capable of classifying a segmented object as a candidate nucleus and filtering out the incorrectly identified segments. Copyright 2022 Inderscience Enterprises Ltd.
International Journal of Biomedical Engineering and Technology, Vol-38, No. 3, pp. 249-266.
https://doi.org/10.1504/IJBET.2022.121739 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128739597&doi=10.1504%2fIJBET.2022.121739&partnerID=40&md5=b34d7968c2cf4da98adb1dd3f93f35ea
Vincent S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Chandra J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India