LAUSR

PURPOSE Dental shade matching by using digital images may be feasible when suitable color features are properly manipulated. Separating the color features into feature spaces facilitates favorable matching. We propose using support vector machines (SVM), which are outstanding classifiers, in shade classification. METHODS A total of 1300 shade tab images were captured using a smartphone camera with auto-mode settings and no flash. The images were shot at angled distances of 14-20cm from a shade guide at a clinic equipped with light tubes that produced a 4000K color temperature. The Group 1 samples comprised 1040 tab images, for which the shade guide was randomly positioned in the clinic, and the Group 2 samples comprised 260 tab images, for which the shade guide had a fixed position in the clinic. Rectangular content was cropped manually on each shade tab image and further divided into 10×2 blocks. The color features extracted from the blocks were described using a feature vector. The feature vectors in each group underwent SVM training and classification by using the "leave-one-out" strategy. RESULTS The top one and three accuracies of Group 1 were 0.86 and 0.98, respectively, and those of Group 2 were 0.97 and 1.00, respectively. CONCLUSIONS This study provides a feasible technique for dental shade classification that uses the camera of a mobile device. The findings reveal that the proposed SVM classification might outperform the shade-matching results of previous studies that have performed similarity measurements of ΔE levels or used an S, a*, b* feature set.