Estimation of subcutaneous and visceral fat tissue volume on abdominal MR images

Neural Network Applications in Electrical Engineering(2014)

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Abstract
Fat depots at different location are associated with variable metabolic risks. It has been noted that visceral abdominal adipose tissue contributes more to these risks than subcutaneous adipose tissue. For discrimination between subcutaneous and visceral adipose tissue contemporary studies primarily use cross sectional medical imaging. Fat volume at different anatomical locations is usually identified and determined either manually or in semiautomatic manner. In this study we combined different image processing methods for unsupervised discrimination of subcutaneous and visceral adipose tissue on abdominal T1 MR images. Procedure has been tested on 16 subjects and results are compared with visceral and subcutaneous volume obtained by semiautomatic method from the literature. High correlation was achieved for subcutaneous fat tissue volume (0.98) while for visceral fat tissue good correlation has been noted (0.86).
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Key words
biological tissues,biomedical mri,medical image processing,unsupervised learning,abdominal mr images,cross sectional medical imaging,image processing methods,semiautomatic method,subcutaneous fat tissue volume estimation,unsupervised discrimination,variable metabolic risks,visceral fat tissue volume estimation,adipose tissue,mri,dynamic programming,fuzzy c-mean,magnetic resonance imaging,image segmentation
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