• DocumentCode
    665119
  • Title

    Automatic human brain MRI volumetric analysis technique using EM-algorithm

  • Author

    Nazari, Mina Rafi ; Singh, Y. Premkumar

  • Author_Institution
    Fac. of Inf. Technol., Multimedia Univ., Cyberjaya, Malaysia
  • fYear
    2013
  • fDate
    21-23 Oct. 2013
  • Firstpage
    79
  • Lastpage
    83
  • Abstract
    The paper presents automated volumetric analysis of human brain MR images for many applications based on the Expectation-maximization (EM) algorithm. It involves voxel labeling, counting, and calculating tissues volume. The voxel labeling requires the brain magnetic resonance image segmentation which is most commonly performed based on voxels intensity signals. A widely used method for segmentation is by creating a Gaussian Mixture Model (GMM) through the EM algorithm and the same can be used to find the tissues, class label and volumes. The experimental results are provided for volumetric analysis of automated segmentation of male and female subjects as well as normal volumes of tissue classes for verifying correctness of automated volumetric analysis and statistical inference for diagnostic applications.
  • Keywords
    Gaussian processes; biomedical MRI; expectation-maximisation algorithm; image segmentation; medical image processing; mixture models; statistical analysis; EM-algorithm; GMM; Gaussian mixture model; automatic human brain MRI volumetric analysis technique; brain magnetic resonance image segmentation; diagnostic applications; expectation-maximization algorithm; statistical inference; tissues volume calculation; voxel counting; voxel labeling; voxels intensity signal; Algorithm design and analysis; Biomedical imaging; Brain modeling; Image segmentation; Magnetic resonance imaging; Standards; Automatic Volumetric analysis; Brain tissue models; Expectation maximization (EM); Guassian mixture model; Magnetic resonance imaging (MRI);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotic and Sensors Environments (ROSE), 2013 IEEE International Symposium on
  • Conference_Location
    Washington, DC
  • Print_ISBN
    978-1-4673-2938-5
  • Type

    conf

  • DOI
    10.1109/ROSE.2013.6698422
  • Filename
    6698422