{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T16:20:49Z","timestamp":1758817249386},"reference-count":44,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Wireless capsule endoscopy (WCE) is a non-invasive technique used to examine the interiors of digestive tracts. Generally, the digestive tract can be divided into four segments: the entrance; stomach; small intestine; and large intestine. The stomach and the small intestine have a higher risk of infections than the other segments. In order to locate the diseased organ, an appropriate classification of the WCE images is necessary. In this article, a novel method is proposed for automatically locating the pylorus in WCE. The location of the pylorus is determined on two levels: rough-level and refined-level. In the rough-level, a short-term color change at the boundary between stomach and intestine can help us to find approximately 70\u2013150 positions. In the refined-level, an improved Weber local descriptor (WLD) feature extraction method is designed for gray-scale images. Compared to the original WLD calculation method, the method for calculating the differential excitation is improved to give a higher level of robustness. A K-nearest neighbor (KNN) classifier is incorporated to segment these images around the approximate position into different regions. The proposed algorithm locates three most probable positions of the pylorus that were marked by the clinician. The experimental results indicate that the proposed method is effective.<\/jats:p>","DOI":"10.1515\/bmt-2015-0080","type":"journal-article","created":{"date-parts":[[2016,4,24]],"date-time":"2016-04-24T00:58:02Z","timestamp":1461459482000},"page":"1-12","source":"Crossref","is-referenced-by-count":5,"title":["A novel method for automatically locating the pylorus in the wireless capsule endoscopy"],"prefix":"10.1515","volume":"62","author":[{"given":"Shangbo","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Abubakar","family":"Siddique","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","reference":[{"key":"ref31","first-page":"II","volume":"volume 2","year":"2006","journal-title":"Colour and Texture Based Gastrointestinal Tissue Discrimination"},{"key":"ref11","first-page":"A76","article-title":"Can we detect when a Wireless Capsule Endoscope WCE leaves the stomach using computational colour techniques? 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