Content-Adaptive Region-Based Color Texture Descriptors for Medical Images

The design of computer-assisted decision (CAD) systems for different biomedical imaging scenarios is a challenging task in computer vision. Sometimes, this challenge can be attributed to the image acquisition mechanisms since the lack of control on the cameras can create different visualizations of the same imaging site under different rotation, scaling, and illumination parameters, with a requirement to get a consistent diagnosis by the CAD systems. Moreover, the images acquired from different sites have specific colors, making the use of standard color spaces highly redundant. In this paper, we propose to tackle these issues by introducing novel region-based texture, and color descriptors. The proposed texture features are based on the usage of analytic Gabor filters (for compensation of illumination variations) followed by the calculation of first- and second-order statistics of the filter responses and making them invariant using some trivial mathematical operators. The proposed color features are obtained by compensating for the illumination variations in the images using homomorphic filtering followed by a bag-of-words approach to obtain the most typical colors in the images. The proposed features are used for the identification of cancer in images from two distinct imaging modalities, i.e., gastroenterology and dermoscopy. Experiments demonstrate that the proposed descriptors compares favorably to several other state-of-the-art methods, elucidating on the effectiveness of adapted features for image characterization.