A methodological approach to the classification of dermoscopy images.
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%.
Celebi
M Emre
ME
Department of Computer Science and Engineering, The University of Texas at Arlington, Room 300, Nedderman Hall, Arlington, TX 76019-0015, USA. celebi@cse.uta.edu
Kingravi
Hassan A
HA
Uddin
Bakhtiyar
B
Iyatomi
Hitoshi
H
Aslandogan
Y Alp
YA
Stoecker
William V
WV
Moss
Randy H
RH
D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*