Melanoma and seborrheic keratosis differentiation using texture features.
OBJECTIVE
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.


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.
Deshabhoina
Srinivas V
SV
Department of Electrical and Computer Engineering, Southern Illinois University at Edwardsville, IL 62026, USA.
Umbaugh
Scott E
SE
Stoecker
William V
WV
Moss
Randy H
RH
Srinivasan
Subhashini K
SK
D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000592:standards*; D017492:Keratosis, Seborrheic / Q000175:diagnosis*; D008545:Melanoma / Q000175:diagnosis*; D015203:Reproducibility of Results; D012878:Skin Neoplasms / Q000175:diagnosis*