Wavelet and statistical analysis for melanoma classification

Document Type

Article

Publication Date

1-1-2002

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. In order to select features that are statistically correlated, normally distributed features were compared using an unpaired t-test and non-normally distributed features were compared using the Wilcoxon rank-sum test. 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 fractions 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%. © 2002 SPIE · 1605-7422/02/$15.00.

Identifier

0036029984 (Scopus)

Publication Title

Proceedings of SPIE the International Society for Optical Engineering

External Full Text Location

https://doi.org/10.1117/12.467098

ISSN

0277786X

First Page

1346

Last Page

1353

Volume

4684 III

This document is currently not available here.

Share

COinS