• DocumentCode
    636455
  • Title

    A quantitative technique for assessing the change in severity over time in psoriatic lesions using computer aided image analysis

  • Author

    Juan Lu ; Kazmierczak, Ed ; Manton, Jonathan H. ; Sinclair, Robert

  • Author_Institution
    Dept. of Comput. & Inf. Syst., Univ. of Melbourne, Melbourne, VIC, Australia
  • fYear
    2013
  • fDate
    3-7 July 2013
  • Firstpage
    2380
  • Lastpage
    2383
  • Abstract
    Psoriasis is a chronic skin disease affecting an estimated 125 million people worldwide. One of the key problems in the management of this condition is the objective measurement of lesion severity over time. Currently, severity is scored by clinicians using visual protocols leading to intra and inter observer variability that makes measurement of treatment efficacy subjective. In this paper, an automatic computer aided image analysis system is proposed that quantitatively assess the changes of erythema and scaling severity of psoriatic lesions in long-term treatment. The algorithm proposed in this paper works on 2D digital images by selecting features that can be used to accurately segment erythema and scaling in psoriasis lesions and assess their changes in severity, according to the popular psoriasis area and severity index (PASI). The algorithms are validated by developing linear models that correlate well with changes in severity scores given by dermatologists. To the best of our knowledge, no such computer assisted method for psoriasis severity assessment in a long-term treatment exists.
  • Keywords
    biomedical optical imaging; diseases; image segmentation; medical image processing; skin; 2D digital images; PASI; automatic computer aided image analysis system; chronic skin disease; erythema severity changes; image segmentation; lesion severity measurement; psoriasis area and severity index; psoriasis management; psoriasis severity assessment; psoriatic lesions; quantitative technique; scaling severity changes; severity change assessment; Computers; Image color analysis; Image segmentation; Indexes; Lesions; Linear regression; Skin;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
  • Conference_Location
    Osaka
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2013.6610017
  • Filename
    6610017