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

Abstract

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.