Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS

Abstract

This paper is a study of using Spatial Grey- Level Co-occurrence Matrix (SGLCM) and First-Order Statistics (FOS), for characterization of liver tissue. SGLCM and FOS are applied on three modalities of liver images, consisting of Magnetic Resonance Imaging (MRI), Ultrasound and Computed Tomography (CT), for the diagnosis of liver diseases. The results indicate that the proposed texture analysis methodology is able to characterize cyst, fatty liver and healthy liver in clinical test images with high success rates. The study indicates viable use of SGLCM and FOS in multimode image analysis and development of a texture-based multimode computer- aided diagnostic (CAD) system for liver diseases.