Content-based image retrieval algorithm for nuclei segmentation in histopathology images CBIR algorithm for histopathology image segmentation

• Published in: Multimedia tools and applications Vol. 80; no. 2; pp. 3017 - 3037
• Main Authors: Kurmi, Yashwant, Chaurasia, Vijayshri
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Multimedia Information Systems; Special Purpose and Application-Based Systems; Microscopic image segmentation; Contour enhancement; Content-based image retrieval (CBIR); Nuclei segmentation; Histopathology images
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09797-3

In today’s world, the medical diagnostic system shows a high reliance on medical imagery and digital nosology. To facilitate the fast and precise screening of samples, technology is leading towards the computer-aided disease diagnosis and grading. Image segmentation possesses high worth in the computer-aided disease diagnosis and grading systems to extract the region of interest. This paper presents a content-based image retrieval algorithm for histopathology image segmentation for identification and extraction of nuclei. The proposed technique furnishes nuclei segmentation in three cascaded stages; pre-processing, nuclei points and region refining, and composite nuclei segmentation. The performance of nuclei segmentation is investigated on six hematoxylins and eosin (H&E) stained histopathology images datasets. Simulation outcomes of the segmentation schemes confirm the superiority of the proposed method for nuclei segmentation in histopathology images in qualitative and quantitative analysis.