Comparison of several texture features for tumor detection in CE images

Capsule endoscopy (CE) has been widely used as a new technology to diagnose gastrointestinal tract diseases, especially for small intestine. However, the large number of images in each test is a great burden for physicians. As such, computer aided detection (CAD) scheme is needed to relieve the workload of clinicians. In this paper, automatic differentiation of tumor CE image and normal CE image is investigated through comparative textural feature analysis. Four different color textures are studied in this work, i.e., texture spectrum histogram, color wavelet covariance, rotation invariant uniform local binary pattern and curvelet based local binary pattern. With support vector machine being the classifier, the discrimination ability of these four different color textures for tumor detection in CE images is extensively compared in RGB, Lab and HSI color space through ten-fold cross-validation experiments on our CE image data. It is found that HSI color space is the most suitable color space for all these texture based CAD systems. Moreover, the best performance achieved is 83.50% in terms of average accuracy, which is obtained by the scheme based on rotation invariant uniform local binary pattern.