id: 05978917
dt: a
an: 05978917
au: Liu, Zhao; Smith, Lyndon; Sun, Jiuai; Smith, Melvyn; Warr, Robert
ti: Biological indexes based reflectional asymmetry for classifying cutaneous
    lesions.
so: Fichtinger, Gabor (ed.) et al., Medical image computing and
    computer-assisted intervention ‒ MICCAI 2011. 14th international
    conference, Toronto, Canada, September 18‒22, 2011. Proceedings, Part
    III. Berlin: Springer (ISBN 978-3-642-23625-9/pbk). Lecture Notes in
    Computer Science 6893, 124-132 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-23626-6_16
ab: Summary: This paper proposes a novel reflectional asymmetry descriptor to
    quantize the asymmetry of the cutaneous lesions for the discrimination
    of malignant melanoma from benign nevi. A pigmentation elevation model
    of the biological indexes is first constructed, and then the asymmetry
    descriptor is computed by minimizing the histogram difference of the
    global point signatures of the pigmentation model. Melanin and Erythema
    Indexes are used instead of the original intensities in colour space to
    characterize the pigmentation distribution of the cutaneous lesions.
    311 dermoscopy images are used to validate the algorithm performance,
    where 88.50\% sensitivity and 81.92\% specificity have been achieved
    when employing an SVM classifier.
rv: