Generalized gray level dependence method for prostate cancer classification

In this paper, we present a new approach for multi-spectral texture classification. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a Spatial and Spectral Gray Level Dependence Method (SSGLDM) in order to extend the concept of spatial gray level dependence method by assuming texture joint information between spectral bands. In addition, the new texture features measurement related to (SSGLDM) which define the image properties have been also proposed. Extensive experiments have been carried out on many multispectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the Gray Level Co-occurrence Matrix (GLCM). The results indicate a significant improvement in classification accuracy.