TY - JFULL AU - Lorena Urricelqui and Armando Malanda and Arantxa Villanueva PY - 2009/11/ TI - Automatic Segmentation of Thigh Magnetic Resonance Images T2 - International Journal of Biomedical and Biological Engineering SP - 313 EP - 320 VL - 3 SN - 1307-6892 UR - https://publications.waset.org/pdf/14805 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 34, 2009 N2 - Purpose: To develop a method for automatic segmentation of adipose and muscular tissue in thighs from magnetic resonance images. Materials and methods: Thirty obese women were scanned on a Siemens Impact Expert 1T resonance machine. 1500 images were finally used in the tests. The developed segmentation method is a recursive and multilevel process that makes use of several concepts such as shaped histograms, adaptative thresholding and connectivity. The segmentation process was implemented in Matlab and operates without the need of any user interaction. The whole set of images were segmented with the developed method. An expert radiologist segmented the same set of images following a manual procedure with the aid of the SliceOmatic software (Tomovision). These constituted our 'goal standard'. Results: The number of coincidental pixels of the automatic and manual segmentation procedures was measured. The average results were above 90 % of success in most of the images. Conclusions: The proposed approach allows effective automatic segmentation of MRIs from thighs, comparable to expert manual performance. ER -