B-Scan Images Analyzed By CNN and Co-Occerrence Matrix
In this paper, we combine cellular neural network (CNN) and gray step co-occurrence matrix to process B-scan images of fatty patients' livers. We deal with the B-scan images of fatty patients' livers by the edge detection cellular neural network, and then analyze the B-scan image features, including the co-occurrence matrix's contrast (Contrast), correlation (Correlation), energy (Energy) and homogeneity (Homogeneity). The value of Contrast on 0deg direction seems to correlate to the degree of the damage of patients' livers. It is expected that the method provided in this paper will be helpful to the diagnosis of biomedical images.