Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network.
Epiluminescence microscopy (ELM) is a non-invasive technique for in vivo examination which can provide additional criteria for the clinical diagnosis of pigmented skin lesions (PSLs). In the present study we attempt to determine whether PSLs can be automatically diagnosed by an integrated computerized system. This system should recognize the PSL, automatically extract features and use these features in training an artificial neural network, which should--if sufficiently trained--be capable of recognizing and classifying a new PSL without human aid. One hundred and twenty images of randomly selected histologically proven PSLs (33 common naevi, 48 dysplastic naevi and 39 malignant melanomas) were used in this study. The images were digitally obtained and the morphological features of the PSLs were extracted electronically without human assistance. The numerical data were then divided into learning and testing cases and linked to an artificial neural network for training and for further classification of lesions that the system had not been trained on. Our results show that the computerized system was able to automatically identify 95% of the PSLs presented. The sensitivity and specificity of the computerized system were 90% and 74% respectively. In contrast, when differentiating between individual types of lesions, the system performed at true positive rates of only 38% for malignant melanoma, 62% for dysplastic naevi and 33% for common naevi. Our data indicate that (1) ELM images of PSLs provide an excellent source for digital image analysis; (2) the vast majority of PSLs can be correctly identified by a relatively simple (and thus not "intelligent") application of digital image analysis; (3) automatic feature extraction based mainly on ABCD rules provides reliable data on the distinction between benign and malignant PSLs; and (4) there is evidence that artificial neural networks can be trained to adequately discriminate between benign and malignant PSLs.
Binder
M
M
Department of Dermatology, University of Vienna Medical School, Austria. michael.binder@akh-wien.ac.at
Kittler
H
H
Seeber
A
A
Steiner
A
A
Pehamberger
H
H
Wolff
K
K
D004416:Dysplastic Nevus Syndrome / Q000145:classification* / Q000473:pathology; D005188:False Negative Reactions; D005189:False Positive Reactions; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008492:Medical Informatics Computing; D008545:Melanoma / Q000145:classification* / Q000473:pathology; D008853:Microscopy / Q000379:methods*; D016571:Neural Networks, Computer; D009508:Nevus, Pigmented / Q000145:classification* / Q000473:pathology; D012878:Skin Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000473:pathology; D017216:Telemedicine / Q000379:methods