14. Generalized Gaussian Density: An Efficient Tool for Medical Images Texture Analysis

This chapter introduces a novel approach for early cancer detection using generalized Gaussian density (GGD) and a supervised classifier, specifically a neural network with Bayesian back-propagation. The method is applied to medical images of breast abnormalities, lung nodules, and bone lesions, demonstrating high accuracy in tumor detection. The core concept behind GGD is wavelet decomposition, which enables multi-resolution analysis and effective texture analysis. The proposed approach involves preprocessing, segmentation, and classification, with the GGD parameters forming the feature vector for classification. The results show high recognition rates for lung nodules, breast masses, and microcalcifications, outperforming previous methods. The text also provides a comprehensive comparison with other techniques, highlighting the efficiency and accuracy of the GGD-based approach. This method proves to be faster and more accurate, making it a valuable tool for early cancer detection and potentially improving patient outcomes.