Comparison and Evaluation of Methods for Liver Tumor Classification from CT Datasets

This paper proposes an automatic system for early detection of liver diseases from Computed tomography (CT) images. The general Computer Aided Diagnosis (CAD) system, including liver diagnosis can be done by segmenting a liver and lesion, extracting features and classify disease whether it is hepatoma or hemangioma. To segment a liver from CT abdominal images histogram analyzer and morphological operation is used. Then to extract a lesion from liver Fuzzy c-mean (FCM) clustering is used. In feature extraction biorthogonal wavelet, Gray-level co-occurrence matrix (GLCM) and fast discrete curvelet transform (FDCT) techniques are used. The textural information obtained was used to train various neural network such as Back propagation Neural Network (BPN), Probabilistic Neural Network (PPN) and Cascade feed forward BPN (CFBPN).The outcome obtained from neural networks are compared with each other to find best combination of features and neural network