Ulcer detection in wireless capsule endoscopy images using bidimensional nonlinear analysis

Abstract

Wireless Capsule Endoscopy (WCE) constitutes a recent technological breakthrough that enables the observation of the gastrointestinal tract (GT) and especially the entire small bowel in a non-invasive way compared to the traditional imaging techniques. WCE allows a detailed inspection of the intestine and identification of its clinical lesions. However, the main drawback of this method is the time consuming task of reviewing the vast amount of images produced. To address this, a novel technique for discriminating abnormal endoscopic images related to ulcer, the most common disease of GT, is presented here. Towards this direction, the Bidimensional Ensemble Empirical Mode Decomposition (BEEMD) was applied to images of the small bowel acquired by a WCE system in order to extract their Intrinsic Mode Functions (IMFs). The IMFs reveal differences in structure from their finest to their coarsest scale providing a new analysis domain. Additionally, lacunarity analysis was employed as a method to quantify and extract the texture patterns of the images and differentiate the ulcer from the healthy regions. Experimental results demonstrated promising classification accuracy (>90%), exhibiting a high potential towards WCE analysis. © 2010 International Federation for Medical and Biological Engineering.