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

Acharya, U.R. and Fujita, H. and Bhat, S. and Raghavendra, U. and Gudigar, A. and Molinari, F. and Vijayananthan, A. and Ng, K.H. (2016) Decision support system for fatty liver disease using GIST descriptors extracted from ultrasound images. Information Fusion, 29. pp. 32-39. ISSN 1566-2535, DOI

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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 disease 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 to eliminate the trivial features and provides the distinctive features for qualitative classification. Finally the clinically significant features are fused using classifiers such as decision tree (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) of 0.9674 correctly.

Item Type: Article
Uncontrolled Keywords: Fatty liver disease; MFA; Liver cirrhosis; GIST descriptors; PNN; Spatial envelope energy spectrum
Subjects: R Medicine
T Technology > TA Engineering (General). Civil engineering (General)
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Oct 2017 03:19
Last Modified: 23 Oct 2017 03:19

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