Image and Statistical Analysis of Melanocytic Histology


Aims: We apply digital image analysis techniques to study selected types of melanocytic lesions. Methods and Results: We use advanced digital image analysis to compare melanocytic lesions. All comparisons were statistically significant (p < 0.0001) and we highlight four: 1) melanoma to nevi, 2) melanoma subtypes to nevi, 3) severely dysplastic nevi to other nevi, and 4) melanoma to severely dysplastic nevi. We were successful in differentiating melanoma from nevi (ROC area 0.95) using image-derived features. Analysis revealed features related to nuclear size, shape, and distance between nuclei most important. Dividing melanoma into subtypes, even greater separation was obtained (ROC area 0.98 for superficial spreading melanoma; 0.95 for lentigo maligna melanoma; and 0.99 for unclassified). Severely dysplastic nevi were best differentiated from conventional and mildly dysplastic nevi by differences in cellular staining qualities (ROC area 0.84). We found that melanoma were separated from severely dysplastic nevi by features related to cell shape and cellular staining qualities (ROC area 0.95). Conclusions: We offer a unique perspective into the evaluation of melanocytic lesions and demonstrate a technological application with increasing prevalence, with potential use as an adjunct to traditional diagnosis in the future.

Marc Niethammer
Marc Niethammer
Professor of Computer Science

My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.