In this paper we classify of liver diseases using Gray Level Co-occurrence Matrix (GLCM) to extract classification features from CT images. The techniques used here included Mean, variance, contrast, energy, entropy, correlation.To find the gray level variations in CT images, it complements the GLCM features extracted from CT images can be used to estimate the size distribution of the subpatterns. Linear discriminant analysis was used for the tissue classification. In This study found that the liver diseases texture reveals a different underlying pattern compared to the normal liver and other abdominal tissues with classification sensitivity and specificity 85.2% and 88.9% respectively, and the combination of the texture features throughout the different tri-phasic image phases provide the highest predictive. overall accuracy of 83.8 % using linear discriminant analysis. These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate organ names.
