A Methodological Approach to the Classification of Dermoscopy Images

Abstract

In this paper a methodological approach to the classification of pigmented skin lesions in dermoscopy images is presented. First, automatic border detection is performed to separate the lesion from the background skin. Shape features are then extracted from this border. For the extraction of color and texture related features, the image is divided into various clinically significant regions using the Euclidean distance transform. This feature data is fed into an optimization framework, which ranks the features using various feature selection algorithms and determines the optimal feature subset size according to the area under the ROC curve measure obtained from support vector machine classification. The issue of class imbalance is addressed using various sampling strategies, and the classifier generalization error is estimated using Monte Carlo cross validation. Experiments on a set of 564 images yielded a specificity of 92.34% and a sensitivity of 93.33%.

Department(s)

Chemistry

Second Department

Electrical and Computer Engineering

Sponsor(s)

EDRA Interactive Atlas of Dermoscopy
James A. Schlipmann Melanoma Cancer Foundation
National Institutes of Health (U.S.)
National Science Foundation (U.S.)
Texas Workforce Commission

Keywords and Phrases

Machine learning; Skin -- Cancer

International Standard Serial Number (ISSN)

0895-6111

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2007 Elsevier, All rights reserved.

Publication Date

01 Jan 2007

Link to Full Text

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