Comparative Analysis of Textural Features Derived from GLCM for Ultrasound Liver Image Classification

Comparative analysis of nine textural feature measures derived from gray-level co-occurrence matrix obtained from the region(s) of interest (ROI) among the normal and abnormal anatomical structures that appear in the patient’s ultrasound liver images is presented in this paper. Selection of the most robust discriminating features for classification experiment is performed through analysis of each feature classes’ separability power. The results analysis shows that cluster prominence, cluster shade, maximum probability, and entropy have high classes’ separability power and were selected for the classification of liver ultrasound images into normal liver (NL), primary liver cell carcinoma (PLCC) and hepatocellular carcinoma (HCC) at 0.4, 0.4, 0.2 and 0.6 sensitivity respectively. Keywords— Liver tissue, Feature extraction, Feature selection.

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