Open AccessUse of adaptive hybrid filtering process in Crohn's disease lesion detection from real capsule endoscopy videos
Author(s) -
Charisis Vasileios S.,
Hadjileontiadis Leontios J.
Publication year - 2016
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2015.0055
Subject(s) - computer science , artificial intelligence , curvelet , capsule endoscopy , feature extraction , pattern recognition (psychology) , computer vision , support vector machine , radiology , medicine , wavelet transform , wavelet
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.