Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images. Issue 6 (11th April 2019)

• Record Type: Journal Article
• Authors: Charfi, Said; El Ansari, Mohamed; Balasingham, Ilangko
• Is Part Of: IET image processing. Volume 13:Issue 6(2019)
• Page Start: 1023
• Page End: 1030
• Publication Date: 2019-04-11
• Subjects: support vector machines -- endoscopes -- multilayer perceptrons -- biomedical optical imaging -- diseases -- hidden Markov models -- image classification -- image texture -- feature extraction -- medical image processing -- image segmentation; segmented regions -- computer‐aided diagnosis system -- ulcer detection -- wireless capsule endoscopy -- gastrointestinal tract -- traditional endoscopies -- diagnosis time -- detection accuracy -- CAD system -- WCE images -- input images -- saliency map‐based texture -- colour -- ulcerous regions; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6232
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Wireless capsule endoscopy (WCE) has revolutionised the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large number of images to be inspected by the clinicians. Hence, the design of a computer‐aided diagnosis (CAD) system will have a great potential to help reduce the diagnosis time and improve the detection accuracy. To address this problem, the authors propose a CAD system for automatic detection of ulcer in WCE images. Firstly, they enhance the input images to be better exploited in the main steps of the proposed method. Afterward, segmentation using saliency map‐based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden Markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.