title: Automated classification of liver disorders using ultrasound images.
abstract: This paper presents a novel approach for detection of Fatty liver disease (FLD) and Heterogeneous liver using textural analysis of liver ultrasound images. The proposed system is able to automatically assign a representative region of interest (ROI) in a liver ultrasound which is subsequently used for diagnosis. This ROI is analyzed using Wavelet Packet Transform (WPT) and a number of statistical features are obtained. A multi-class linear support vector machine (SVM) is then used for classification. The proposed system gives an overall accuracy of ~95% which clearly illustrates the efficacy of the system.
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA. fayyazafsar@gmail.com
authors.forename: Fayyaz ul Amir Afsar
authors.initials: Fu
authors.lastname: Minhas
authors.forename: Durre
authors.initials: D
authors.lastname: Sabih
authors.forename: Mutawarra
authors.initials: M
authors.lastname: Hussain
mesh_terms: D005234:Fatty Liver / Q000145:classification* / Q000175:diagnosis / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D014463:Ultrasonography; D058067:Wavelet Analysis
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