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

Healthcare Technology Letters

article

Vasileios S. Charisis¹  Leontios J. Hadjileontiadis¹ 

[1] Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki

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;   

DOI  :  10.1049/htl.2015.0055

学科分类：肠胃与肝脏病学

来源: Wiley

【 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.

【 授权许可】

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