DocumentCode :
1800024
Title :
Estimation of subcutaneous and visceral fat tissue volume on abdominal MR images
Author :
Spasojevic, Aleksandar ; Stojanov, Oliver ; Turukalo, Tatjana Loncar ; Sveljo, Olivera
Author_Institution :
Dept. of Power, Electron. & Telecommun., Univ. of Novi Sad, Novi Sad, Serbia
fYear :
2014
fDate :
25-27 Nov. 2014
Firstpage :
217
Lastpage :
220
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).
Keywords :
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; Dynamic programming; Heuristic algorithms; Image edge detection; Image segmentation; Magnetic resonance imaging; Nonhomogeneous media; Adipose tissue; MRI; dynamic programming; fuzzy c-mean;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2014 12th Symposium on
Conference_Location :
Belgrade
Print_ISBN :
978-1-4799-5887-0
Type :
conf
DOI :
10.1109/NEUREL.2014.7011511
Filename :
7011511
Link To Document :
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