Melanoma and seborrheic keratosis differentiation using texture features
Article first published online: 24 OCT 2003
Skin Research and Technology
Volume 9, Issue 4, pages 348–356, November 2003
How to Cite
Deshabhoina, S. V., Umbaugh, S. E., Stoecker, W. V., Moss, R. H. and Srinivasan, S. K. (2003), Melanoma and seborrheic keratosis differentiation using texture features. Skin Research and Technology, 9: 348–356. doi: 10.1034/j.1600-0846.2003.00044.x
- Issue published online: 24 OCT 2003
- Article first published online: 24 OCT 2003
- Accepted for publication 28 March 2003
- classification rules;
- computer vision;
- seborrheic keratosis;
- second-order histogram features;
- texture analysis
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. Over-all, the seborrheic keratosis images were better identified by the texture features than the melanoma images.