The Role of the Multiresolution Textural Features in Improving the Characterization and Recognition of the Liver Tumors, Based on Ultrasound Images

The malignant tumors are complex structures, which evolve chaotically, invading the entire human body. The gold standard for cancer diagnosis is the biopsy, but this is invasive, dangerous. We elaborated non-invasive, computerized methods, for tumor characterization, based on ultrasound images. We defined the textural model of the malignant tumors, consisting of the relevant textural features, able to distinguish these structures from similar tissues, and of the specific values associated to the relevant features [1]. In this paper, we analyzed the role that some multiresolution textural features have in improving the liver tumors' diagnosis accuracy. In the new attribute set we added features derived from the second and superior order GLCM and edge-based statistics, all computed after applying the Wavelet transform. The experiments were performed on ultrasound images of patients suffering from hepatocellular carcinoma and from benign liver tumors, considering also the aspect of the cirrhotic parenchyma where the tumors evolve.