TY - JOUR
T1 - Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy
T2 - A feasibility study
AU - Elbaum, Marek
AU - Kopf, Alfred W.
AU - Rabinovitz, Harold S.
AU - Langley, Richard G.B.
AU - Kamino, Hideko
AU - Mihm, Martin C.
AU - Sober, Arthur J.
AU - Peck, Gary L.
AU - Bogdan, Alexandru
AU - Gutkowicz-Krusin, Dina
AU - Greenebaum, Michael
AU - Keem, Sunguk
AU - Oliviero, Margaret
AU - Wang, Steven
N1 - Funding Information:
Supported in part by grant numbers 2R44 CA/AR60229-02, 1R43 CA74628-01 and 2R44 CA74628-02 from the National Cancer Institute to Electro-Optical Sciences, Inc, and by the Christopher Columbus Fellowship Foundation Award for 1998 (to M. E.).
PY - 2001
Y1 - 2001
N2 - Background: Differentiation of melanoma from melanocytic nevi is difficult even for skin cancer specialists. This motivates interest in computer-assisted analysis of lesion images. Objective: Our purpose was to offer fully automatic differentiation of melanoma from dysplastic and other melanocytic nevi through multispectral digital dermoscopy. Method: At 4 clinical centers, images were taken of pigmented lesions suspected of being melanoma before biopsy. Ten gray-level (MelaFind) images of each lesion were acquired, each in a different portion of the visible and near-infrared spectrum. The images of 63 melanomas (33 invasive, 30 in situ) and 183 melanocytic nevi (of which 111 were dysplastic) were processed automatically through a computer expert system to separate melanomas from nevi. The expert system used either a linear or a nonlinear classifier. The "gold standard" for training and testing these classifiers was concordant diagnosis by two dermatopathologists. Results: On resubstitution, 100% sensitivity was achieved at 85% specificity with a 13-parameter linear classifier and 100%/73% with a 12-parameter nonlinear classifier. Under leave-one-out cross-validation, the linear classifier gave 100%/84% (sensitivity/specificity), whereas the nonlinear classifier gave 95%/68%. Infrared image features were significant, as were features based on wavelet analysis. Conclusion: Automatic differentiation of invasive and in situ melanomas from melanocytic nevi is feasible, through multispectral digital dermoscopy.
AB - Background: Differentiation of melanoma from melanocytic nevi is difficult even for skin cancer specialists. This motivates interest in computer-assisted analysis of lesion images. Objective: Our purpose was to offer fully automatic differentiation of melanoma from dysplastic and other melanocytic nevi through multispectral digital dermoscopy. Method: At 4 clinical centers, images were taken of pigmented lesions suspected of being melanoma before biopsy. Ten gray-level (MelaFind) images of each lesion were acquired, each in a different portion of the visible and near-infrared spectrum. The images of 63 melanomas (33 invasive, 30 in situ) and 183 melanocytic nevi (of which 111 were dysplastic) were processed automatically through a computer expert system to separate melanomas from nevi. The expert system used either a linear or a nonlinear classifier. The "gold standard" for training and testing these classifiers was concordant diagnosis by two dermatopathologists. Results: On resubstitution, 100% sensitivity was achieved at 85% specificity with a 13-parameter linear classifier and 100%/73% with a 12-parameter nonlinear classifier. Under leave-one-out cross-validation, the linear classifier gave 100%/84% (sensitivity/specificity), whereas the nonlinear classifier gave 95%/68%. Infrared image features were significant, as were features based on wavelet analysis. Conclusion: Automatic differentiation of invasive and in situ melanomas from melanocytic nevi is feasible, through multispectral digital dermoscopy.
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U2 - 10.1067/mjd.2001.110395
DO - 10.1067/mjd.2001.110395
M3 - Article
AN - SCOPUS:17744389766
SN - 0190-9622
VL - 44
SP - 207
EP - 218
JO - Journal of the American Academy of Dermatology
JF - Journal of the American Academy of Dermatology
IS - 2
ER -
