A novel regression model from RGB image data to spectroradiometric correlates optimized for tooth colored shades

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• 作者：Melody N. Carney ; ^{Carney.150@osu.edu" class="auth_mail" title="E-mail the corresponding author} ; William M. Johnston
• 关键词：Digital images ; RGB ; Color matching ; Regression model ; Color accuracy ; CIEDE2000 ; CIEL*a*b* ; Spectroradiometry
• 刊名：Journal of Dentistry
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：51
• 期：Complete
• 页码：45-48
• 全文大小：510 K

文摘

Objectives of this study were to correlate RGB data from the VITA Linearguide 3D Master and VITA Bleached Guide 3D Master shade guides with their spectroradiometric correlates through a regression model while indicating a methodology for validation of accuracy of digital imaging systems. Additional objectives were to provide summary RGB data and to determine a relationship between lightness and RGB values for these shade guides.

Methods

Radiant energy measurements and images were taken with a Canon Rebel T3i and Macro Ring Lite MR-14EX for each shade tab. RGB data was extracted using Image J and compared with spectroradiometric measurements. Regression models relating the RGB data to spectroradiometric counterparts in CIE XYZ and absolute reflectance were developed using SAS 9.3. Image data was statistically analyzed to determine a relationship between RGB values and lightness.

Results

Regression models with R² values greater than 0.99 for RGB to XYZ and greater than 0.95 for RGB to absolute reflectance were developed. Summary RGB data for the shade guides including Pearson correlation coefficients ranging between −0.92 and −0.97 for RGB related to lightness was determined.

Conclusions

A relationship between RGB and lightness for the shade guides was found. Regression models were developed that allow tooth color information to be translated from digital images to accurate shade tab correlates for color matching purposes in dentistry. This allows for optimal color accuracy when using digital imaging to translate color information and provides a method of validating digital imaging systems for color accuracy.