Diagnostic Advances
Copyright ©The Author(s) 2016. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Oct 21, 2016; 22(39): 8641-8657
Published online Oct 21, 2016. doi: 10.3748/wjg.v22.i39.8641
Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images
Vasileios S Charisis, Leontios J Hadjileontiadis
Vasileios S Charisis, Leontios J Hadjileontiadis, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR54124 Thessaloniki, Greece
Leontios J Hadjileontiadis, Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, PO Box 127788, United Arab Emirates
Author contributions: Charisis VS designed the texture feature extraction procedure, namely DLac, and wrote the manuscript; Hadjileontiadis LJ designed the hybrid adaptive filtering procedure and made revisions to the manuscript.
Conflict-of-interest statement: No potential conflicts of interest relevant to this article were reported.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Leontios J Hadjileontiadis, PhD, Professor, Composer/Musicologist, Director of the Signal Processing and Biomedical Technology Unit (SPBTU), Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, Build. D, 6th floor, GR54124 Thessaloniki, Greece. leontios@auth.gr
Telephone: +30-231-0996340 Fax: +30-231-0996312
Received: July 13, 2016
Peer-review started: July 16, 2016
First decision: August 19, 2016
Revised: September 2, 2016
Accepted: September 14, 2016
Article in press: September 14, 2016
Published online: October 21, 2016
Abstract

A new feature extraction technique for the detection of lesions created from mucosal inflammations in Crohn’s disease, based on wireless capsule endoscopy (WCE) images processing is presented here. More specifically, a novel filtering process, namely Hybrid Adaptive Filtering (HAF), was developed for efficient extraction of lesion-related structural/textural characteristics from WCE images, by employing Genetic Algorithms to the Curvelet-based representation of images. Additionally, Differential Lacunarity (DLac) analysis was applied for feature extraction from the HAF-filtered images. The resulted scheme, namely HAF-DLac, incorporates support vector machines for robust lesion recognition performance. For the training and testing of HAF-DLac, an 800-image database was used, acquired from 13 patients who undertook WCE examinations, where the abnormal cases were grouped into mild and severe, according to the severity of the depicted lesion, for a more extensive evaluation of the performance. Experimental results, along with comparison with other related efforts, have shown that the HAF-DLac approach evidently outperforms them in the field of WCE image analysis for automated lesion detection, providing higher classification results, up to 93.8% (accuracy), 95.2% (sensitivity), 92.4% (specificity) and 92.6% (precision). The promising performance of HAF-DLac paves the way for a complete computer-aided diagnosis system that could support physicians’ clinical practice.

Keywords: Capsule endoscopy, Curvelet, Ulcer, Genetic algorithms, Crohn’s disease

Core tip: This paper presents a novel procedure to analyze wireless capsule endoscopy (WCE) images and extract features towards the automatic detection of Crohn’s disease-based lesions. In this direction, a hybrid adaptive filtering process is proposed that aims to refine the WCE images, prior to feature extraction, by selecting via a genetic algorithm approach the most informative curvelet-based components of the images. Then, differential lacunarity is employed for extracting color-texture features in YCbCr color space. The experimental results showed that the proposed WCE image analysis scheme is robust and outperforms related approaches of the literature, mainly in the case of mild lesions detection.