Segmentation of fascias, fat and muscle from magnetic resonance images in humans: the DISPIMAG software

被引:0
|
作者
J. P. Mattei
Y. Le. Fur
N. Cuge
S. Guis
P. J. Cozzone
D. Bendahan
机构
[1] Université de la Méditerranée,CRMBM – UMR CNRS 6612 Faculté de Médecine
关键词
Image processing; Muscle; Fat; MRI; Cross sectional area;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:275 / 279
页数:4
相关论文
共 50 条
  • [1] Segmentation of fascias, fat and muscle from magnetic resonance images in humans: the DISPIMAG software
    Mattei, J. P.
    Le Fur, Y.
    Cuge, N.
    Guis, S.
    Cozzone, P. J.
    Bendahan, D.
    MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE, 2006, 19 (05) : 275 - 279
  • [2] AUTOMATIC 3-D MUSCLE AND FAT SEGMENTATION OF THIGH MAGNETIC RESONANCE IMAGES IN INDIVIDUALS WITH SPINAL CORD INJURY
    Mesbah, Samineh
    Shalaby, Ahmed
    Willhite, Andrea
    Harkema, Susan
    Rejc, Enrico
    El-baz, Ayman
    2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2017, : 3280 - 3284
  • [3] Brain magnetic resonance images segmentation
    Zhou Zhenyu
    Ruan Zongcai
    2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2005, : 3078 - 3081
  • [4] Residual Semantic Segmentation of the Prostate from Magnetic Resonance Images
    Hossain, Md Sazzad
    Paplinski, Andrew P.
    Betts, John M.
    NEURAL INFORMATION PROCESSING (ICONIP 2018), PT VII, 2018, 11307 : 510 - 521
  • [5] Automatic segmentation of the glenohumeral cartilages from magnetic resonance images
    Neubert, A.
    Yang, Z.
    Engstrom, C.
    Xia, Y.
    Strudwick, M. W.
    Chandra, S. S.
    Fripp, J.
    Crozier, S.
    MEDICAL PHYSICS, 2016, 43 (10) : 5370 - 5379
  • [6] A Parallel Segmentation of Brain Tumor from Magnetic Resonance Images
    Dessai, Vidhya S.
    Arakeri, Megha P.
    Guddeti, Ram Mohana Reddy
    2012 THIRD INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION & NETWORKING TECHNOLOGIES (ICCCNT), 2012,
  • [7] Towards Left Ventricle Segmentation From Magnetic Resonance Images
    Dakua, Sarada Prasad
    IEEE SENSORS JOURNAL, 2017, 17 (18) : 5971 - 5981
  • [8] Unsupervised Brain Tumor Segmentation from Magnetic Resonance Images
    Ouchicha, Chaimae
    Ammor, Ouafae
    Meknassi, Mohammed
    2019 INTERNATIONAL CONFERENCE ON WIRELESS NETWORKS AND MOBILE COMMUNICATIONS (WINCOM), 2019, : 111 - 115
  • [9] Software for interactive segmentation of the carotid artery from 3D black blood magnetic resonance images
    Jin, Y
    Ladak, HM
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2004, 75 (01) : 31 - 43
  • [10] Thigh muscle segmentation of chemical shift encoding-based water-fat magnetic resonance images: The reference database MyoSegmenTUM
    Schlaeger, Sarah
    Freitag, Friedemann
    Klupp, Elisabeth
    Dieckmeyer, Michael
    Weidlich, Dominik
    Inhuber, Stephanie
    Deschauer, Marcus
    Schoser, Benedikt
    Bublitz, Sarah
    Montagnese, Federica
    Zimmer, Claus
    Rummeny, Ernst J.
    Karampinos, Dimitrios C.
    Kirschke, Jan S.
    Baum, Thomas
    PLOS ONE, 2018, 13 (06):