Abstract

Purpose: To explore texture features in two-dimensional images to differentiate seborrheic keratosis from melanoma.

Methods: A systematic approach to consistent classification of skin tumors is described. Texture features, based on the second-order histogram, were used to identify the features or a combination of features that could consistently differentiate a malignant skin tumor (melanoma) from a benign one (seborrheic keratosis). Two hundred and seventy-one skin tumor images were separated into training and test sets for accuracy and consistency. Automatic induction was applied to generate classification rules. Data analysis and modeling tools were used to gain further insight into the feature space.

Result and Conclusions: In all, 85-90% of seborrheic keratosis images were correctly differentiated from the malignant skin tumors. The features correlation_average, correlation_range, texture_energy_average and texture_energy_range were found to be the most important features in differentiating seborrheic keratosis from melanoma. Overall, the seborrheic keratosis images were better identified by the texture features than the melanoma images.

Department(s)

Chemistry

Second Department

Electrical and Computer Engineering

Sponsor(s)

National Institutes of Health (U.S.). Small Business Innovation Research Program
Stoecker and Associates

Comments

This research was funded in part by an SBIR Phase II grant from the National Institutes of Health through a subcontract from Stoecker and Associates, Rolla, Missouri, USA, SIUE account #2-70252.
This is the peer reviewed version of the following article: Melanoma and Seborrheic Keratosis Differentiation using Texture Features, which has been published in final form at http://dx.doi.org/10.1034/j.1600-0846.2003.00044.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

Keywords and Phrases

Accuracy; Article; Controlled Study; Data Analysis; Histogram; Image Analysis; Melanoma; Seborrheic Keratosis; Skin Tumor; Statistical Model; Tumor Classification; Diagnosis, Differential; Humans; Image Processing, Computer-Assisted; Keratosis, Seborrheic; Reproducibility Of Results; Skin Neoplasms; Classification Rules; Computer Vision; Second-Order Histogram Features; Texture Analysis

International Standard Serial Number (ISSN)

0909-752X; 1600-0846

Document Type

Article - Journal

Document Version

Accepted Manuscript

File Type

text

Language(s)

English

Rights

© 2003 John Wiley & Sons, All rights reserved.

PubMed ID

14641886

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