An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images

Acharya, U.R. and Fujita, H. and Sudarshan, V.K. and Mookiah, M.R.K. and Koh, J.E.W. and Tan, J.H. and Hagiwara, Y. and Chua, C.K. and Junnarkar, S.P. and Vijayananthan, A. and Ng, K.H. (2016) An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images. Information Fusion, 31. pp. 43-53. ISSN 1566-2535, DOI

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Alcoholic and non-alcoholic fatty liver disease is one of the leading causes of chronic liver diseases and mortality in Western countries and Asia. Ultrasound image assessment is most commonly and widely used to identify the Non-Alcoholic Fatty Liver Disease (NAFLD). It is one of the faster and safer non-invasive methods of NAFLD diagnosis available in imaging modalities. The diagnosis of NAFLD using biopsies is expensive, invasive, and causes anxiety to the patients. The advent of advanced image processing and data mining techniques have helped to develop faster, efficient, objective, and accurate decision support system for fatty liver disease using ultrasound images. This paper proposes a novel feature extraction models based on Radon Transform (RT) and Discrete Cosine Transform (DCT). First, Radon Transform (RT) is performed on the ultrasound images for every 1 degree to capture the low frequency details. Then 2D-DCT is applied on the Radon transformed image to obtain the frequency features (DCT coefficients). Further the 2D-DCT frequency coefficients (features) obtained are converted to 1D coefficients vector in zigzag fashion. This 1D array of DCT coefficients are subjected to Locality Sensitive Discriminant Analysis (LSDA) to reduce the number of features. Then these features are ranked using minimum Redundancy and Maximum Relevance (mRMR) ranking method. Finally, highly ranked minimum numbers of features are fused using Decision Tree (DT), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Support Vector Machine (SVM), Fuzzy Sugeno (FS) and AdaBoost classifiers to get the highest classification performance. In this work, we have obtained an average accuracy, sensitivity and specificity of 100% in the detection of NAFLD using FS classifier. Also, we have devised an integrated index named as Fatty Liver Disease Index (FLDI) by fusing two significant LSDA components to distinguish normal and FLD class with single number.

Item Type: Article
Uncontrolled Keywords: Fatty liver disease; Ultrasound; Discrete cosine transform; Locality sensitive discriminant analysis; Fuzzy classifier
Subjects: R Medicine
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Faculty of Medicine
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Oct 2017 02:58
Last Modified: 23 Oct 2017 02:58

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