Computer-based classification of chromoendoscopy images using homogeneous texture descriptors.

Computer-aided analysis of clinical pathologies is a challenging task in the field of medical imaging. Specifically, the detection of abnormal regions in the frames collected during an endoscopic session is difficult. The variations in the conditions of image acquisition, such as field of view or illumination modification, make it more demanding. Therefore, the design of a computer-assisted diagnostic system for the recognition of gastric abnormalities requires features that are robust to scale, rotation, and illumination variations of the images. Therefore, this study focuses on designing a set of texture descriptors based on the Gabor wavelets that will cope with certain image dynamics. The proposed features are extracted from the images and utilized for the classification of the chromoendoscopy (CH) frames into normal and abnormal categories. Moreover, to attain a higher accuracy, an optimized subset of descriptors is selected through the genetic algorithm. The results obtained using the proposed features are compared with other existing texture descriptors (e.g., local binary pattern and homogeneous texture descriptors). Furthermore, the selected features are used to train the support vector machine (SVM), naive Bayes (NB) algorithm, k-nearest neighbor algorithm, linear discriminant analysis, and ensemble tree classifier. The performance of these state-of-the-art classifiers for different texture descriptors is compared based on the accuracy, sensitivity, specificity, and area under the curve (AUC) derived by using the CH images. The classification results reveal that the SVM classifier achieves 90.0% average accuracy and 0.93 AUC when it is employed with an optimized set of features obtained by using a genetic algorithm.