Deep scattering convolution network based features for ultrasonic fatty liver tissue characterization

R, Bharath and P, Rajalakshmi (2017) Deep scattering convolution network based features for ultrasonic fatty liver tissue characterization. In: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 11-15 July 2017, Jeju Island, South Korea.

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Accumulation of excess fat in the liver tissue is the leading cause for dysfunction of liver, which can lead to the diseases from fibrosis to end stage cirrhosis. Hence, early detection of fatty liver becomes crucial in avoiding the liver from permanent failure. Depending on the concentration of fat in the tissue, the liver is classified as Normal, Grade 1, Grade 2 and Grade 3 respectively. The texture of liver tissue in ultrasound image is so specific to the concentration of fat, hence classifying the fatty liver is formulated as a texture discrimination problem. In this paper, we present an automated algorithm for grading the tissue of a fatty liver based on the features obtained from the invariant scattering convolution network (ISCN). ISCN, which involves cascade of modulus complex wavelet transforms and averaging operations results in scattering coefficients (SC), these coefficients will give stable invariant representations and also maps the texture of fatty liver image to a discriminative manifold giving good features for classification. SC are of high dimension and hence a compact representation feature is obtained by summing all the SC coefficients. Summed SC features along with cubic SVM classifier gave an accuracy of 96.6% in automatically categorizing the fatty content present in the tissue of a liver.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Liver, Scattering, Wavelet transforms, Support vector machines, Convolution, Fats, Classification algorithms
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 17 Jul 2017 11:50
Last Modified: 19 Sep 2017 11:51
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