الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
This work integrates preprocessing and segmentation steps with SVM classifier. Further, to extract the subtle sonographic information, the contrast of the US image is enhanced by using a new methodology which is a spatial filter but nonlinear one called bilateral filter. In the present work, large numbers of features are extracted by using statistical, textural and histogram-based features to maximally discriminate the FLL by developing a SVM classification system. Another major contribution of this work is development of an automatic identification and classification of various stages of focal liver lesions based on the Multi-Support Vector Machine (Multi-SVM). The proposed system can be used to discriminate focal liver diseases such as Cyst, Hemangioma, and Hepatocellular carcinoma along with normal liver. The multi-class scenario is a composition of a series of two-class problems, using one-against-all which is the earliest and one of the most widely used implementations. selection of ROI significantly impact the classification performances, thus we proposes an automatic ROI selection using fuzzy c-means initialized by level set technique. For multi-class classification, we adopt the One-Against-All (OAA) method. The proposed Multi-SVM based system is compared with the k-Nearest Neighbor (kNN) based approaches. Experimental results have demonstrated that the Multi-SVM based system greatly outperforms kNN-based approaches and other methods in the literature. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of focal liver lesion diseases