• DocumentCode
    3243805
  • Title

    MRI brain classification using support vector machine

  • Author

    Othman, Mohd Fauzi Bin ; Abdullah, Noramalina Bt ; Kamal, Nurul Fazrena Bt

  • Author_Institution
    Centre for Artificial Intell. & Robot. (CAIRO), Univ. Teknol. Malaysia, Kuala Lumpur, Malaysia
  • fYear
    2011
  • fDate
    19-21 April 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images similar to query image in content. Magnetic resonance imaging (MRI) is an imaging technique that has played an important role in neuroscience research for studying brain images. Classification is an important part in retrieval system in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. In this paper, we have obtained the feature related to MRI images using discrete wavelet transformation. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed.
  • Keywords
    biomedical MRI; brain; discrete wavelet transforms; image classification; medical image processing; support vector machines; tumours; MRI brain classification; SVM; brain tumor; classification system; discrete wavelet transformation; kernel based technique; magnetic resonance imaging; medical image retrieval system; medical imaging; neuroscience; support vector machine; Biomedical imaging; Brain; Feature extraction; Magnetic resonance imaging; Support vector machines; Wavelet transforms; Brain Tumor; Classification; MRI; SVM; Wavelet;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-1-4577-0003-3
  • Type

    conf

  • DOI
    10.1109/ICMSAO.2011.5775605
  • Filename
    5775605