Dental shade matching method based on hue, saturation, value color model with machine learning and fuzzy decision

Color information is an important indicator of color matching. It is recommended to use hue (H) and saturation (S) to improve the accuracy of color analysis. The proposed method for dental shade matching in this study is based on the hue, saturation, value (HSV) color model. To evaluate the performance of the proposed method in matching dental shades, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), composite peak signal-to-noise ratio (CPSNR), and S-CIELAB (Special International Commission on Illumination, L* for lightness, a* from green to red, and b* from blue to yellow) were utilized. To further improve the performance of the proposed method, dental image samples were multiplied by the weighted coefficients derived by training the model using machine learning to reduce errors. Thus, the PSNR of 97.64% was enhanced to 99.93% when applied with the proposed fuzzy decision model. Results show that the proposed method based on the new fuzzy decision technology is effective and has an accuracy of 99.78%, which is a significant improvement of previous results. The new fuzzy decision is a method that combines the HSV color model, PSNR(H), PSNR(S), and SSIM information, which are used for the first time in research on tooth color matching. Results show that the proposed method performs better than previous methods.