Journal article

Image and statistical analysis of melanocytic histology

Jayson Miedema, James Stephen Marron, Marc Niethammer, David Borland, John Woosley, Jason Coposky, Susan Wei, Howard Reisner, Nancy E Thomas

HISTOPATHOLOGY | WILEY | Published : 2012

Abstract

AIMS: We applied digital image analysis techniques to study selected types of melanocytic lesions. METHODS AND RESULTS: We used advanced digital image analysis to compare melanocytic lesions as follows: (i) melanoma to nevi, (ii) melanoma subtypes to nevi, (iii) severely dysplastic nevi to other nevi and (iv) melanoma to severely dysplastic nevi. We were successful in differentiating melanoma from nevi [receiver operating characteristic area (ROC) 0.95] using image-derived features, among which those related to nuclear size and shape and distance between nuclei were most important. Dividing melanoma into subtypes, even greater separation was obtained (ROC area 0.98 for superficial spreading ..

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University of Melbourne Researchers

Grants

Awarded by University Cancer Research Fund, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC


Awarded by NIH


Awarded by NIH NIBIB


Awarded by NATIONAL CANCER INSTITUTE


Awarded by NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING


Awarded by NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES


Awarded by NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES


Funding Acknowledgements

This work was supported, in part, by the University Cancer Research Fund, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, CA112243, CA11243-05S109 and P30ES010126. Susan Wei was supported by an NSF Graduate Fellowship and NIH Predoctoral Training Program in Bioinformatics and Computational Biology, T32 GM067553-05S1. Marc Niethammer was supported by NIH NIBIB 5P41EB002025-27.