Automatic detection of small bowel tumors in endoscopic capsule images by ROI selection based on discarded lightness information

This paper addresses the problem of automatic detection of tumoral frames in endoscopic capsule videos by using features directly extracted from the color space. We show that tumor can be appropriately discriminated from normal tissue by using only color information histogram measures from the Lab color space and that light saturated regions are usually classified as tumoral regions when color based discriminative procedures are used. These regions are correctly classified if lightening is discarded becoming the tissue classifier based only on the color differences a and b of the Lab color space. While current state of the art systems for small bowel tumor detection usually rely on the processing of the whole frame regarding features extraction this paper proposes the use of fully automatic segmentation in order to select regions likely to contain tumoral tissue. Classification is performed by using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) by using features from color channels a and b of the Lab color space. The proposed algorithm outperforms in more than 5% a series of other algorithms based on features obtained from the higher frequency components selected from Wavelets and Curvelets transforms while saving important computational resources. In a matter of fact the proposed algorithm is more than 25 times faster than algorithms requiring wavelet/curvelet and co-occurrence computations.