• DocumentCode
    1254757
  • Title

    Model creation and deformation for the automatic segmentation of the brain in MR images

  • Author

    Aboutanos, Georges B. ; Nikanne, Jyrki ; Watkins, Nancy ; Dawan, B.M.

  • Author_Institution
    Div. of Inf. Syst., Harris Corp., Melbourne, FL, USA
  • Volume
    46
  • Issue
    11
  • fYear
    1999
  • Firstpage
    1346
  • Lastpage
    1356
  • Abstract
    In this paper a method for the automatic segmentation of the brain in magnetic resonance images is presented and validated. The proposed method involves two steps: 1) the creation of an initial model, and 2) the deformation of this model to fit the exact contours of the brain in the images. A new method to create the initial model has been developed and compared to a more traditional approach in which initial models are created by means of brain atlases. A comprehensive validation of the complete segmentation method has been conducted on a series of three-dimensional T1-weighted magnetization-prepared rapid gradient echo image volumes acquired both from control volunteers and patients suffering from Cushing´s disease. This validation study compares results obtained with the method the authors propose and contours drawn manually. Averages differences between manual and automatic segmentation with the model creation method the authors propose are 1.7% and 2.7% for the control volunteers and the Cushing´s patients, respectively. These numbers are 1.8% and 5.6% when the atlas-based method is used.
  • Keywords
    biomedical MRI; brain models; diseases; image segmentation; medical image processing; 3D T1-weighted magnetization-prepared rapid gradient echo image volumes; Cushing´s disease patients; MRI; atlas-based method; automatic segmentation; brain MR images; control volunteers; magnetic resonance imaging; medical diagnostic imaging; model creation; model deformation; Automatic control; Biomedical imaging; Brain modeling; Deformable models; Image edge detection; Image processing; Image segmentation; Magnetic resonance; Surface fitting; Volume measurement; Algorithms; Brain; Cushing Syndrome; False Negative Reactions; False Positive Reactions; Humans; Magnetic Resonance Imaging; Models, Neurological; Observer Variation; Reference Values; Reproducibility of Results;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/10.797995
  • Filename
    797995