• DocumentCode
    2939485
  • Title

    Brain lesion detection in MRI with fuzzy and geostatistical models

  • Author

    Pham, Tuan D.

  • Author_Institution
    Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia
  • fYear
    2010
  • fDate
    Aug. 31 2010-Sept. 4 2010
  • Firstpage
    3150
  • Lastpage
    3153
  • Abstract
    Automated image detection of white matter changes of the brain is essentially helpful in providing a quantitative measure for studying the association of white matter lesions with other types of biomedical data. Such study allows the possibility of several medical hypothesis validations which lead to therapeutic treatment and prevention. This paper presents a new clustering-based segmentation approach for detecting white matter changes in magnetic resonance imaging with particular reference to cognitive decline in the elderly. The proposed method is formulated using the principles of fuzzy c-means algorithm and geostatistics.
  • Keywords
    biomedical MRI; brain; cognition; fuzzy set theory; geriatrics; image segmentation; medical image processing; statistical analysis; MRI; brain lesion detection; clustering-based segmentation; cognition; elderly; fuzzy c-means algorithm; geostatistical models; magnetic resonance imaging; white matter lesions; Biomedical imaging; Computed tomography; Image segmentation; Lesions; Magnetic resonance imaging; Manuals; Senior citizens; Algorithms; Artificial Intelligence; Brain; Brain Neoplasms; Computer Simulation; Data Interpretation, Statistical; Female; Fuzzy Logic; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Models, Biological; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
  • Conference_Location
    Buenos Aires
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-4123-5
  • Type

    conf

  • DOI
    10.1109/IEMBS.2010.5627188
  • Filename
    5627188