A Hybrid Approach to Diagnosis of Hepatic Tumors in Computed Tomography Images

Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year, can be reduced by early detection and diagnosis. Computer-aided liver analysis can help in the early detection and diagnosis of liver cancer. In this paper, enhancement and segmentation process is applied to increase the computation and focus on liver parenchyma. This parenchyma also segmented using Watershed and Region Growing algorithms to extract liver tumors. These tumors will be analyzed and characterized to distinguish between hemangioma (benign) and hepatocellular (malignant) tumors using Local Binary Pattern (LBP), Gray Level Co-occurrence matrix (GLCM), Fractal Dimension (FD) and feature fusion technique is applied to maximize and enhance the performance of the classifier rate. The authors review different methods for liver segmentation and abnormality classification. An attempt was made to combine the individual scores from different techniques in order to compensate their individual weaknesses and to preserve their strength. The authors present and exhaustively evaluate algorithms using computer vision techniques. The experimental results based on confusion matrix and kappa coefficient show that the higher accuracy is obtained of automatic agreement classification and suggest that the developed CAD system has great potential and promise in the automatic diagnosis of both benign and malignant tumors of liver.