Wavelet and statistical analysis for melanoma classification

Amit Nimunkar; Atam P. Dhawan; Patricia A. Relue; Sachin V. Patwardhan

doi:10.1117/12.467098

9 May 2002 Wavelet and statistical analysis for melanoma classification

Amit Nimunkar, Atam P. Dhawan, Patricia A. Relue, Sachin V. Patwardhan

Proceedings Volume 4684, Medical Imaging 2002: Image Processing; (2002) https://doi.org/10.1117/12.467098
Event: Medical Imaging 2002, 2002, San Diego, California, United States

Abstract

The present work focuses on spatial/frequency analysis of epiluminesence images of dysplastic nevus and melanoma. A three-level wavelet decomposition was performed on skin-lesion images to obtain coefficients in the wavelet domain. A total of 34 features were obtained by computing ratios of the mean, variance, energy and entropy of the wavelet coefficients along with the mean and standard deviation of image intensity. An unpaired t-test for a normal distribution based features and the Wilcoxon rank-sum test for non-normal distribution based features were performed for selecting statistically correlated features. For our data set, the statistical analysis of features reduced the feature set from 34 to 5 features. For classification, the discriminant functions were computed in the feature space using the Mahanalobis distance. ROC curves were generated and evaluated for false positive fraction from 0.1 to 0.4. Most of the discrimination functions provided a true positive rate for melanoma of 93% with a false positive rate up to 21%.

Citation Download Citation

Amit Nimunkar, Atam P. Dhawan, Patricia A. Relue, and Sachin V. Patwardhan "Wavelet and statistical analysis for melanoma classification", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); https://doi.org/10.1117/12.467098

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