The Role of the Complex Extended Textural Microstructure Co-occurrence Matrix in the Unsupervised Detection of the HCC Evolution Phases, based on Ultrasound Images

The hepatocellular carcinoma (HCC) is a frequent malignant liver tumour and one of the main causes of death. Detecting the HCC evolution phases is an important issue, aiming the early diagnosis of this tumour and patient monitoring with maximum accuracy. Our objective is to discover the evolution stages of HCC, through unsupervised classification techniques, using advanced texture analysis methods. In this work, we assessed the role that the Haralick features derived from the Complex Extended Textural Microstructure Co-occurrence Matrices (CETMCM) have in the unsupervised detection of the HCC evolution stages. A textural model for these phases was also generated. The obtained results were validated by supervised classifiers, well known for their performance, such as the Multilayer Perceptron (MLP), Support Vector Machines (SVM), respectively decision trees and they were also compared with the previously obtained results in this domain. The final classification accuracy was about 90%.