A curvelet-based lacunarity approach for ulcer detection from Wireless Capsule Endoscopy images

Wireless Capsule Endoscopy (WCE) is a fairly new technology that offers a low-risk, non invasive visual inspection of the patient´s digestive tract, especially the small bowel, that was previously unreachable using the traditional endoscopic methods. However, the large amount of images produced by WCE requires a highly trained physician to manually inspect them; a procedure that is time consuming and prone to human error. This was the rationale to propose a novel strategy for automatic detection of WCE images related to ulcer, one of the most common findings of the digestive tract. This paper introduces a new texture extraction method based on the Discrete Curvelet Transform (DCT), a recent multi-resolution analysis tool. Textural information is acquired by calculating the lacunarity index of DCT subbands of the WCE images. The classification step is performed by a Support Vector Machine (SVM), demonstrating promising classification accuracy (86.5%) and pointing towards further research in this field.