Healthcare Technology Letters | |
Use of adaptive hybrid filtering process in Crohn's disease lesion detection from real capsule endoscopy videos | |
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】
CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND
【 预 览 】
Files | Size | Format | View |
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RO202107100001055ZK.pdf | 307KB | download |