Intrinsic higher-order correlation and lacunarity analysis for WCE-based ulcer classification
Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patient's digestive tract, especially small intestine. However, the time-consuming task of reviewing the endoscopic data is a burden for the physicians. This limitation was the motive to propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract. Towards this direction, WCE data are processed with Bidimensional Ensemble Empirical Mode Decomposition to reveal their inherent structural components, and also to reconstruct a new refined image. Then, texture information is extracted by analyzing the intrinsic second/higher-order correlation of the original image and by calculating the lacunarity index of the refined image. Experimental results demonstrated promising classification accuracy (97%) exhibiting high potential towards a complete computer-aided diagnosis system.