Mattei J P, Fur Y Le, Cuge N, Guis S, Cozzone P J, Bendahan D
CRMBM - UMR CNRS 6612 Faculté de Médecine, Université de la Méditerranée, 27, Bd Jean Moulin, 13385, Marseille Cedex 5, France.
MAGMA. 2006 Nov;19(5):275-9. doi: 10.1007/s10334-006-0051-1. Epub 2006 Sep 27.
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.
