Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patient's digestive tract and, especially, small intestine. However, reviewing the endoscopic data is time consuming and requires intense labor of highly experienced physicians. These limitations were 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 color-rotated in order to boost the chromatic attributes of ulcer regions. Then, texture information is extracted by utilizing the local binary pattern operator that analyses the spatial structure of the images at a very local level. Experimental results demonstrated promising classification accuracy (91.1%) exhibiting high potential towards a complete computer-aided diagnosis system that will not only reduce WCE data reviewing time, but also serve as an assisting tool for the training of inexperienced physicians.