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Segmentation of Fascias, Fat and Muscle from Magnetic Resonance Images in Humans: the DISPIMAG Software

Overview
Journal MAGMA
Publisher Springer
Date 2006 Sep 28
PMID 17004065
Citations 16
Authors
J P Mattei
Y Le Fur
N Cuge
S Guis
P J Cozzone
D Bendahan
Affiliations
Soon will be listed here.
Abstract

Segmentation of human limb MR images into muscle, fat and fascias remains a cumbersome task. We have developed a new software (DISPIMAG) that allows automatic and highly reproducible segmentation of lower-limb MR images. Based on a pixel intensity analysis, this software does not need any previous mathematical or statistical assumptions. It displays a histogram with two main signals corresponding to fat and muscle, and permits an accurate quantification of their relative spatial distribution. To allow a systematic discrimination between muscle and fat in any subject, fixed boundaries were first determined manually in a group of 24 patients. Secondly, an entirely automatic process using these boundaries was tested by three operators on four patients and compared to the manual approach, showing a high concordance.

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References
1.
Gong Q, Phoenix J, Kemp G, Garcia-Finana M, Frostick S, Brodie D . Estimation of body composition in muscular dystrophy by MRI and stereology. J Magn Reson Imaging. 2000; 12(3):467-75. DOI: 10.1002/1522-2586(200009)12:3<467::aid-jmri13>3.0.co;2-g. View
2.
Mitsiopoulos N, Baumgartner R, Heymsfield S, Lyons W, Gallagher D, Ross R . Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol (1985). 1998; 85(1):115-22. DOI: 10.1152/jappl.1998.85.1.115. View
3.
Barra V, Boire J . Segmentation of fat and muscle from MR images of the thigh by a possibilistic clustering algorithm. Comput Methods Programs Biomed. 2002; 68(3):185-93. DOI: 10.1016/s0169-2607(01)00172-9. View
4.
Beneke R, Neuerburg J, Bohndorf K . Muscle cross-section measurement by magnetic resonance imaging. Eur J Appl Physiol Occup Physiol. 1991; 63(6):424-9. DOI: 10.1007/BF00868073. View
5.
Engstrom C, Loeb G, Reid J, FORREST W, Avruch L . Morphometry of the human thigh muscles. A comparison between anatomical sections and computer tomographic and magnetic resonance images. J Anat. 1991; 176:139-56. PMC: 1260321. View