title: Image analysis of endosocopic ultrasonography in submucosal tumor using fuzzy inference.
abstract: Endoscopists usually make a diagnosis in the submucosal tumor depending on the subjective evaluation about general images obtained by endoscopic ultrasonography. In this paper, we propose a method to extract areas of gastrointestinal stromal tumor (GIST) and lipoma automatically from the ultrasonic image to assist those specialists. We also propose an algorithm to differentiate GIST from non-GIST by fuzzy inference from such images after applying ROC curve with mean and standard deviation of brightness information. In experiments using real images that medical specialists use, we verify that our method is sufficiently helpful for such specialists for efficient classification of submucosal tumors.
authors.affiliation: Department of Computer Engineering, Silla University, Busan 617-736, Republic of Korea.
authors.forename: Kwang Baek
authors.initials: KB
authors.lastname: Kim
authors.forename: Gwang Ha
authors.initials: GH
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D019160:Endosonography; D046152:Gastrointestinal Stromal Tumors / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008067:Lipoma / Q000000981:diagnostic imaging* / Q000473:pathology; D012372:ROC Curve
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