Content-based image retrieval algorithm for nuclei segmentation in histopathology images
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Content-based image retrieval algorithm for nuclei segmentation in histopathology images CBIR algorithm for histopathology image segmentation Yashwant Kurmi1
· Vijayshri Chaurasia2
Received: 21 July 2019 / Revised: 27 August 2020 / Accepted: 2 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Histopathology images · Microscopic image segmentation · Contour enhancement · Content-based image retrieval (CBIR) · Nuclei segmentation
1 Introduction Image segmentation be classified among important processes in the image analysis to emphasize the target area. In the nosology field, image exploration plays an important role in the diagnosis and prognosis of diseases. The research work in various fields of medical imaging is going on to make a perfect diagnosis. Lots of research is going on in the medical Yashwant Kurmi
[email protected] Vijayshri Chaurasia [email protected] 1
Maulana Azad National Institute of Technology, Bhopal, 462003, India
2
Electronics and Communication Engineering Department, Maulana Azad National Institute of Technology, Bhopal, 462003, India
Multimedia Tools and Applications
imaging field to develop a standardized, quantified, and reproducible system for computeraided diagnosis (CAD) [23, 27]. In the segmentation routine, firstly, the seed/key points are extracted to mark the object of interest in the preprocessed image. The tissue deformation study at the microscopic level is termed as histopathology. The captured image at the microscopic level is characterized by histopathology images [HI]. Image preprocessing is an obligatory step in case of a large dataset to transform images of diverse sources to the same platform and provide a single image analysis method for their processing. A preprocessing method provides a compatible solution to process these image varieties by a single image analysis method. The preprocessing may be applied in the form of chromatin morphology, color normalization, and transformation [22] to make suitabl
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