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

作　　者：Venkatesan Rajinikanth Shabnam Mohamed Aslam Seifedine Kadry Orawit Thinnukool 

机构地区：[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

出　　处：《Computers, Materials & Continua》2022年第2期4087-4105,共19页计算机、材料和连续体（英文）

基　　金：Authors of this research thanks the database contributors of CVC-ClinicDB,ETIS-Larib,EndoCV2020,Kvasir for providing the open access to the dataset for research purpose and thank to Deanship of Scientific Research at Majmaah University for supporting this work under the Project No.155/46683.This research work was partially supported by Chiang Mai University.

摘　　要：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 GPfragment,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 fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.

关 键 词：Endoscopic/colonoscopic-image gastric polyp aquila-optimizationalgorithm fuzzy entropy chan-vese segmentation 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]