Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images

Acharya, Rajendra U and Fujita, Hamido and Bhat, Shreya and Raghavendra, U and Gudigar, Anjan and Molinari, Filippo and Vijayananthan, Anusha and Ng Hoong, Kwan (2015) Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images. Information Fusion, 29. pp. 32-39.

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Steatosis or fatty liver disease(FLD) is characterized by the abnormal retention of large vacuoles of neutral fat in the liver cells, either due to alcoholism or metabolic syndrome. Succession of FLD can lead to severe liver diseases such as hepatocellular carcinoma, cirrhosis and hepatic inflammation but it is a reversible dis-ease if diagnosed early. Thus,computer-aided diagnostic tools play a very important role in the automated diagnosis of FLD. This paper focuses on the detection of steatosis and classification of steatotic livers from the normal using ultrasound images. The significant information from the image is extracted using GIST descriptor models. Marginal Fisher Analysis (MFA) integrated with Wilcoxon signed-rank test helps toeliminate the trivial features and provides the distinctive features for qualitative classification. Finally the clinically significant features are fused using classifiers such as decisiontree(DT), support vector machine(SVM), adaBoost , k-nearest neighbor(kNN), probabilistic neural network(PNN), naïve Bayes (NB), fuzzy Sugeno (FS), linear and quadratic discriminant analysis classification of normal and abnormal liver images. Results portray that PNN classifier can diagnose FLD with an average classification accuracy of 98%, 96%sensitivity, 100%specificity and Area Under Curve(AUC) of0.9674correctly

Item Type: Article
Uncontrolled Keywords: Fatty liver disease MFA Liver cirrhosis GIST descriptors PNN Spatial envelope energy spectrum
Subjects: Engineering > MIT Manipal > Instrumentation and Control
Depositing User: MIT Library
Date Deposited: 10 Dec 2015 12:22
Last Modified: 10 Dec 2015 12:22

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