• DocumentCode
    2802720
  • Title

    Automated detection of drusen in the macula

  • Author

    Freund, D.E. ; Bressler, N. ; Burlina, P.

  • Author_Institution
    Johns Hopkins Appl. Phys. Lab., Baltimore, MD, USA
  • fYear
    2009
  • fDate
    June 28 2009-July 1 2009
  • Firstpage
    61
  • Lastpage
    64
  • Abstract
    Age related macular degeneration (AMD) is a condition of the retina that occurs with individuals over 50. AMD is characterized by the formation of drusen in the macula. This condition leads to a deterioration of foveal vision and eventually functional blindness. Automatically screening atrisk individuals may allow the detection of intermediate stage AMD where it is still treatable using anti-VEGH therapy. One of the difficulties in detecting and locating drusen is that their aspect (shape, texture, color, extent) varies significantly, and because of this it is often difficult to build a classifier. To address this difficulty we use a two pronged approach based on (a) multiscale analysis and (b) kernel based anomaly detection. We show experimental results on examples of fundus images taken from healthy and affected patients.
  • Keywords
    eye; medical image processing; support vector machines; vision defects; age related macular degeneration; anti-VEGH therapy; automated detection; automatic screening; classifier; drusen color; drusen extent; drusen shape; drusen texture; foveal vision deterioration; functional blindness; kernel based anomaly detection; multiscale analysis; retina; support vector data description; support vector machine; Biomedical imaging; Blindness; Computer science; Humans; Kernel; Laboratories; Medical treatment; Physics; Retina; Shape; Detection of retinal abnormalities; macular pathologies; support vector methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on
  • Conference_Location
    Boston, MA
  • ISSN
    1945-7928
  • Print_ISBN
    978-1-4244-3931-7
  • Electronic_ISBN
    1945-7928
  • Type

    conf

  • DOI
    10.1109/ISBI.2009.5192983
  • Filename
    5192983