Liver segmentation; grey-wolf optimisation; fuzzy c-means; support vector machine

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Sayed, G. I., A. E. Hassanien, and G. Schaefer, "An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images ", Procedia Computer Science, , vol. 90 , pp. Pages 68-73, 2016. AbstractWebsite

In this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification.