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Video capsule endoscopy produces large-scale color video data of gastrointestinal regions, creating opportunities for context-aware analysis. This work explores abnormality detection by applying fuzzy sets and logic theory to fuzzify multiple color spaces. Examples include retrieval for bleeding detection and polyp vascularization, where preliminary results show promising retrieval performance.",{"@graph":69,"@context":114},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/on-fuzzification-of-color-spaces-for-medical-decision-support-in-video-capsule-endoscopy/315914/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/on-fuzzification-of-color-spaces-for-medical-decision-support-in-video-capsule-endoscopy/315914.png","ImageObject",300,407,{"name":92,"@type":93},"วิน","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108],{"name":109,"@type":110,"acceptedAnswer":111},"What example applications are discussed for evaluation?","Question",{"text":112,"@type":113},"The paper provides retrieval examples for bleeding detection and discusses methodology considerations related to polyp vascularization, with preliminary results indicating promising retrieval quality.","Answer","https://schema.org",{"og:url":83,"og:type":116,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":118,"canonical":83},"index,follow",{"doc_id":120,"site_id":62},315914,1791077455,{"code":4,"msg":5,"data":123},{"doc_id":120,"user_id":124,"nickname":92,"user_avatar":125,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":135,"read_time":136,"preview_page_count":4},2336475104736,"https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222","On Fuzzification of Color Spaces for Medical Decision Support in Video Capsule  \nEndoscopy  \nV. B. Surya Prasath  \nComputational Imaging and Visualization Analysis Lab  \nDepartment of Computer Science  \nUniversity of Missouri-Columbia  \nColumbia MO 65211 USA  \nAbstract  \nAdvances in image and video processing algorithms and the availability of computational resources has paved the way for real-time medical decision support systems to be a reality. Video capsule endoscopy is a direct imaging method for gastrointestinal regions and produces large scale color video data. Fuzzification of color spaces can improve contextual description based tasks that are required in medical decision support. We consider abnormalities detection in video capsule endoscopy using fuzzy sets and logic theory on different colorspaces. Application in retrieval of bleeding detection and polyp vascularization are given as examples of the methodology considered here and preliminary results indicate we obtain promising retrieval results.  \nIntroduction  \nVideo capsule endoscopy (VCE) is a revolutionary imaging technique which paved the way for unprecedented direct visualization of the gastrointestinal tract without much discomfort to patients. A typical colon VCE exam produces around 8 hours of color (RGB) video data. For example, Pillcam Colon capsule endoscope (Given Imaging, Yoqneam, Israel) produces approximately 30 ; 000 frames per patient and more than 1:6 million patients worldwide have used capsule endoscopy over the past 10 years. Automatic algorithms can help augment computer aided diagnosis (CAD) in VCE medical decision support systems and can help reduce the burden on gastroenterologists (Rey 2008; Niwa et al. 2008) . For example, polyp detection (Figueiredo et al. 2010; 2011), mucosa surface identification (Prasath, Figueiredo, and Figueiredo 2011; Prasath et al. 2012; Prasath and Delhibabu 2015b), contrast enhancement (Prasath and Delhibabu 2015a) . Nevertheless, designing automatic methods for automatically analyzing the VCE imagery via image processing and computer vision techniques pose significant challenges as we are dealing with big data.  \nImage processing involves uncertainty quantification and fuzzy techniques are effective in handling various tasks (Kerre and Nachtegael(eds) 2000; Vlachos and Sergiadis 2006; Shamoi, Inoue, and Kawanaka 2014b; 2014a) . Recently, Shamoi et al (Shamoi, Kawanaka, and Inoue 2014) used fuzzification of HSI color space for apparel coordination. In (Shamoi, Kawanaka, and Inoue 2014) a case ex-  \nFigure 1: Each VCE exam involves a human operator (gastroenterologist) looking at various abnormal frames and classifying the impressions using linguistic terms. Automatic classification using a mapping of such linguistic terms to colors using fuzzy logic can be used for various tasks such as image retrieval.  \nample in HSI (Hue, Saturation, Intensity) space is provided for obtaining a correspondence between colors and human impressions. In this work, we adapt the ground-work done in (Shamoi, Kawanaka, and Inoue 2014) to the VCE color space fuzzification for abnormality classifications. We provide an overview of different color spaces (RGB, CMYK, HSV, La*b*) and their fuzzifications to organize all possible human operator color perceptions of abnormalities in VCE images. Using operator defined impressions expressed in terms of linguistic terms we provide retrieval examples. We note the overall framework is general in the sense that it can be expanded with domain knowledge for various related tasks.  \nThe rest of the paper is organized as follows. Next section introduces different color spaces useful in VCE imagery and fuzzification techniques for representing color perceptions of human operators using fuzzy sets. Next, we provide some example classification results on VCE images for bleeding regions.  \nFigure 2: Fuzzy logic and mass assignment theory based mapping of different colors and human (operator) impressions ofthem. Imag","cbCaienNlkZ12hpJ","https://ap.wps.com/l/cbCaienNlkZ12hpJ","pdf",1415881,"English","# Abstract\n# Introduction\n# Color Fuzzification for Abnormalities in Video Capsule Endoscopy\n## Fuzzification","[{\"question\":\"What example applications are discussed for evaluation?\",\"answer\":\"The paper provides retrieval examples for bleeding detection and discusses methodology considerations related to polyp vascularization, with preliminary results indicating promising retrieval quality.\"}]","On Fuzzification of Color Spaces for Medical Decision Support in Video Capsule Endoscopy | PDF",1789907968,13]