@article {130707,
	title = {An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images  },
	journal = {Procedia Computer Science, },
	volume = {  90 },
	year = {2016},
	pages = {Pages 68-73},
	abstract = {<p>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.</p>
},
	url = {http://www.sciencedirect.com/science/article/pii/S1877050916311905},
	author = {Gehad Ismail Sayed and Aboul Ella Hassanien and Gerald Schaefer}
}
