Use of adaptive hybrid filtering process in Crohn's disease lesion detection from real capsule endoscopy videos

[摘要] 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.

[学科分类] 肠胃与肝脏病学

[关键词] diseases;medical image processing;image classification;feature extraction;adaptive filters;endoscopes;image texture;support vector machines;genetic algorithms;Crohn disease lesion detection;capsule endoscopy video;capsule endoscopy image analysis scheme;small bowel ulcer detection;hybrid adaptive filtering process;genetic algorithm;image curvelet-based representation;lesion-related morphological characteristics;differential lacunarity analysis;texture feature extraction;HAF-filtered images;support vector machine;image classification performance;computer-aided diagnosis system