title: Use of adaptive hybrid filtering process in Crohn's disease lesion detection from real capsule endoscopy videos.
abstract: The aim of this Letter is to present a new capsule endoscopy (CE) image analysis scheme for the detection of small bowel ulcers that relate to Crohn's disease. More specifically, this scheme is based on: (i) a hybrid adaptive filtering (HAF) process, that utilises genetic algorithms to the curvelet-based representation of images for efficient extraction of the lesion-related morphological characteristics, (ii) differential lacunarity (DL) analysis for texture feature extraction from the HAF-filtered images and (iii) support vector machines for robust classification performance. For the training of the proposed scheme, namely HAF-DL, an 800-image database was used and the evaluation was based on ten 30-second long endoscopic videos. Experimental results, along with comparison with other related efforts, have shown that the HAF-DL approach evidently outperforms the latter in the field of CE image analysis for automated lesion detection, providing higher classification results. The promising performance of HAF-DL paves the way for a complete computer-aided diagnosis system that could support the physicians' clinical practice.
authors.affiliation: Department of Electrical and Computer Engineering , Aristotle University of Thessaloniki , Thessaloniki 54636 , Greece.
authors.forename: Vasileios S
authors.initials: VS
authors.lastname: Charisis
authors.affiliation: Department of Electrical and Computer Engineering , Aristotle University of Thessaloniki , Thessaloniki 54636 , Greece.
authors.forename: Leontios J
authors.initials: LJ
authors.lastname: Hadjileontiadis
keywords: Crohn disease lesion detection; HAF-filtered images; adaptive filters; capsule endoscopy image analysis scheme; capsule endoscopy video; computer-aided diagnosis system; differential lacunarity analysis; diseases; endoscopes; feature extraction; genetic algorithm; genetic algorithms; hybrid adaptive filtering process; image classification; image classification performance; image curvelet-based representation; image texture; lesion-related morphological characteristics; medical image processing; small bowel ulcer detection; support vector machine; support vector machines; texture feature extraction