{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T07:18:48Z","timestamp":1767165528286,"version":"build-2238731810"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2021,10,18]]},"abstract":"<jats:p>Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.<\/jats:p>","DOI":"10.1155\/2021\/7863113","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T03:30:16Z","timestamp":1634614216000},"page":"1-11","source":"Crossref","is-referenced-by-count":15,"title":["A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3229-2541","authenticated-orcid":true,"given":"Zahra","family":"Amiri","sequence":"first","affiliation":[{"name":"Image Processing and Data Mining Lab, Shahrood University of Technology, Shahrood, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5513-9822","authenticated-orcid":true,"given":"Hamid","family":"Hassanpour","sequence":"additional","affiliation":[{"name":"Image Processing and Data Mining Lab, Shahrood University of Technology, Shahrood, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5595-0615","authenticated-orcid":true,"given":"Azeddine","family":"Beghdadi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University Sorbonne Paris Nord, Villetaneuse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.11591\/ijece.v6i4.9688"},{"key":"2","article-title":"A computer-aided method to detect bleeding frames in capsule Endoscopy images","author":"Z. 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