Skin lesion classification using relative color features.
OBJECTIVE
Clinically, it is difficult to differentiate the early stage of malignant melanoma and certain benign skin lesions due to similarity in appearance. Our research uses image analysis of clinical skin images and relative color-based pattern recognition techniques to enhance the images and improve differentiation of these lesions.


METHODS
First, the relative color images were created from digitized photographic images. Then, they were segmented into objects and morphologically filtered. Next, the relative color features were extracted from the objects to form two different feature spaces; a lesion feature space and an object feature space. The two feature spaces serve as two data models to be analyzed to determine the best features. Finally, we used a statistical analysis model of relative color features, which better classifies the various types of skin lesions.


CONCLUSIONS
The best features found for differentiation of melanoma and benign skin lesions from this study are area, mean in the red and blue bands, standard deviation in the red and green bands, skewness in the green band, and entropy in the red band. With the relative color feature algorithm developed, the best results were obtained with a multi-layer perceptron neural network model. This showed an overall classification success of 79%, with 70% of the benign lesions successfully classified, and 86% of malignant melanoma successfully classified.
Cheng
Yue
Y
Electrical and Computer Engineering Department, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1801, USA.
Swamisai
Ragavendar
R
Umbaugh
Scott E
SE
Moss
Randy H
RH
Stoecker
William V
WV
Teegala
Saritha
S
Srinivasan
Subhashini K
SK
D000465:Algorithms; D003124:Colorimetry / Q000379:methods; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D004416:Dysplastic Nevus Syndrome / Q000175:diagnosis; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000175:diagnosis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009508:Nevus, Pigmented / Q000175:diagnosis; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000145:classification / Q000175:diagnosis*