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Semi/Fully-Automated Segmentation of Gastric-Polyp Using Aquila-Optimization-Algorithm Enhanced Images

Venkatesan Rajinikanth1, Shabnam Mohamed Aslam2, Seifedine Kadry3, Orawit Thinnukool4,*
1 Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering Chennai, 600119, India
2 Department of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
3 Faculty of Applied Computing and Technology, Noroff University College, Kristiansand, 4608, Norway
4 Research Group of Embedded Systems and Mobile Application in Health Science, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
* Corresponding Author: Orawit Thinnukool. Email:

Computers, Materials & Continua 2022, 70(2), 4087-4105. https://doi.org/10.32604/cmc.2022.019786

Received 25 April 2021; Accepted 13 July 2021; Issue published 27 September 2021

Abstract

The incident rate of the Gastrointestinal-Disease (GD) in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image (EI/CI) supported evaluation of the GD is an approved practice. Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity. The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp (GP) with better accuracy. The proposed GP detection system consist; (i) Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy (Fuzzy/Shannon/Kapur) and between-class-variance (Otsu) technique, (ii) Automated (Watershed/Markov-Random-Field) and semi-automated (Chan-Vese/Level-Set/Active-Contour) segmentation of GP fragment, and (iii) Performance evaluation and validation of the proposed scheme. The experimental investigation was performed using four benchmark EI dataset (CVC-ClinicDB, ETIS-Larib, EndoCV2020 and Kvasir). The similarity measures, such as Jaccard, Dice, accuracy, precision, sensitivity and specificity are computed to confirm the clinical significance of the proposed work. The outcome of this research confirms that the fuzzy-entropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.

Keywords

Endoscopic/colonoscopic-image; gastric polyp; aquila-optimization-algorithm; fuzzy entropy; chan-vese segmentation

Cite This Article

V. Rajinikanth, S. Mohamed Aslam, S. Kadry and O. Thinnukool, "Semi/fully-automated segmentation of gastric-polyp using aquila-optimization-algorithm enhanced images," Computers, Materials & Continua, vol. 70, no.2, pp. 4087–4105, 2022.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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