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

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.

Automated summary

This paper presents a content-based image retrieval algorithm for histopathology image segmentation for identification and extraction of nuclei, which confirms the superiority of the proposed method in qualitative and quantitative analysis through performance investigation on six hematoxylins and eosin (H&E) stained histopathology images datasets.