• DocumentCode
    2191261
  • Title

    Classification for Breast MRI Using Support Vector Machine

  • Author

    Wang, Chuin-Mu ; Mai, Xiao-Xing ; Lin, Geng-Cheng ; Kuo, Chio-Tan

  • Author_Institution
    Dept. of Comput. Sci. & Inf. Eng., Nat. Chin-Yi Univ. of Technol., Taichung
  • fYear
    2008
  • fDate
    8-11 July 2008
  • Firstpage
    362
  • Lastpage
    367
  • Abstract
    Magnetic resonance image (MRI) was harmless to the human body and used on the clinical trial extensively in recent years. In this study, we want to detect the tissues of breast form the multi-spectral MR image. Because multi-spectral MR image are scanning the same slice with various frequencies and parameters and it can obtain intact information. In the image classification, we apply support vector machine (SVM) on breast multi-spectral magnetic resonance image to classify the tissues of breast separately. The classification results would assist doctor to judge and sift the breast tumor. In order to further evaluate its performance, the C-means (CM) classification method is compared with SVM. By some experiments, the result of SVM is better than C-mean.
  • Keywords
    biomedical MRI; image classification; medical image processing; support vector machines; C-means classification method; breast MRI classification; breast tissues; magnetic resonance image; multispectral MR image; support vector machine; CM; Classification; SVM; breast MRI;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on
  • Conference_Location
    Sydney, QLD
  • Print_ISBN
    978-0-7695-3242-4
  • Electronic_ISBN
    978-0-7695-3239-1
  • Type

    conf

  • DOI
    10.1109/CIT.2008.Workshops.90
  • Filename
    4568530