Abstract
For successful classification of Hepatocellular Carcinoma (HCC) in ultrasound (US) images, effective preprocessing steps are highly desirable. Most of Computer Aided Diagnostic (CAD) systems miss the most important steps of image preprocessing and image segmentation. In such a framework, only some texture features, which are obtained directly from the images or ROIs, are used as inputs of classifiers. Image preprocessing and segmentation of US images are useful for better judgment of normal and cancerous cases. Although, there are many studies on the classification of medical images, the fully automatic classification is still a difficult task. In this work, we propose an automated classification of US liver tumors using SVM with the aid of Fuzzy c-means (FCM) and level set method. A large number of features were extracted by using statistical, textual, and histogram-based features to discriminate the HCC maximally by developing an SVM classification system. SVMs work on maximizing the margin between the separating hyperplane and the data to minimize upper bound of the generalization error. The proposed Fuzzy C-SVM based system is compared with the K-Nearest Neighbor (KNN) based approach. Experimental results demonstrated that the Fuzzy C-SVM based system greatly outperforms KNN-based approach.
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Ibraheem, M.R., Elmogy, M. (2016). Automated Segmentation and Classification of Hepatocellular Carcinoma Using Fuzzy C-Means and SVM. In: Dey, N., Bhateja, V., Hassanien, A. (eds) Medical Imaging in Clinical Applications. Studies in Computational Intelligence, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-319-33793-7_9
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DOI: https://doi.org/10.1007/978-3-319-33793-7_9
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