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The paper presents an automatic segmentation and classification of liver tumor segmentation in CT images. Computed Tomography (CT) is a standout amongst the most generous medical imaging modalities. CT images are extensively used for liver tumor diagnosis. The precise identification of liver tumor classification and segmentation is based on the accuracy. The decrease in sensitivity, specificity and positive predictive value (PPV) directly affects the accuracy of classification and segmentation. This paper mainly focuses on improving sensitivity, specificity and PPV using an enhanced gray level co-occurrence matrix (GLCM) method. A comparative analysis has been made with three classifiers such as support vector machine(SVM), KNN and Bayesian. The results shows a promising increase in specificity and sensitivity while using SVM. The proposed method achieves 99.4 % sensitivity, 99.6% specificity, 97.03% PPV and hence overall accuracy is 99.5%.

Increasing Sensitivity, Specificity and PPV for Liver Tumor Segmentation and Classification Using Enhanced GLCM