The role of the cooccurrence matrix based on complex extended microstructures in discovering the cirrhosis severity grades within US images

Abstract

Cirrhosis is an important disease, as it can precede liver cancer, and also can lead to death by itself. Detecting the severity grades of cirrhosis is a major issue in this context. The best nowadays standard for this purpose is the biopsy, however this procedure is invasive, dangerous for the patient. Also, there is no objective study in order to establish which the cirrhosis grades are. Our research purpose is to discover the cirrhosis grades using computerized methods and to perform non-invasive, computer assisted and automatic diagnosis of the disease evolution phases. Concerning the employed features, we adopted the texture-based methods, able to emphasize those characteristics of the tissue that cannot be detected by the eye of the medical expert. In this paper, we emphasized the role of the CETMCM Matrix concerning the detection of the cirrhosis severity grades. The method was validated by supervised classification, providing a recognition rate above 95%.