Department of Optics, Faculty of Science, University of Granada, Campus Fuente Nueva, Edificio Mecenas, s/n 18071, Granada, Spain.
Department of Restorative Dentistry and Prosthodontics, Houston Center for Biomaterials and Biomimetics (HCBB), University of Texas School of Dentistry at Houston, 7500 Cambridge St., Ste. 5350, Houston, Texas.
J Esthet Restor Dent. 2018 Mar;30(2):E31-E38. doi: 10.1111/jerd.12376.
To design colorimetric and spectral models of gingival shade guides that adequately represent the color of human gingiva.
A previously compiled database on the spectral reflectance of healthy keratinized gingiva was used for optimization. Coverage Error (CE) and Maximal Error (ME) were optimized using CIELAB and CIEDE2000 color difference formulas. A two-phase process included an FCM algorithm and a nonlinear optimization. A t test was used to compare the performance of the different numbers of clusters/tabs in gingival shade guide models (α = .05).
CIELAB CE and ME for shade guide models with 3 to 6 clusters ranged from 3.1 to 3.9 (P = .028 for 3 vs. 4; and P = .033 for 5 vs. 6 cluster/tab comparison), while the corresponding CIEDE2000 range was from 2.1 to 2.8 (P < .001 for 3 vs. 4 tabs; P < .025 for 4 vs. 5; and P = 0.029 for 5 vs. 6 tab comparisons). The percentage of data points exhibiting a CIELAB color difference lower than the acceptability threshold ranged from 48.7% to 71.4%, and from 52.9% to 82.4%. for CIEDE2000.
An increase in the number of clusters in the gingival shade guide models was associated with a decrease in coverage error (better match) to human gingiva. Gingival shade guide models with only 4 tabs provided a CIELAB and CIEDE2000 coverage error lower than the acceptability threshold for gingival color. Spectral clustering of human gingiva was determined to be valid. CIEDE2000 color difference formula outperformed the CIELAB formula in the optimization process.
Providing a shade guide model with a small number of tabs and a coverage error lower than the 50:50% acceptability threshold would be an optimal solution for shade matching in dentistry. However, no actual gingival or tooth shade guide complies with this. The clustering method, with optimization of both Coverage Error and Maximal Error and spectral clustering that enables more reliable color formulation of cluster representatives of shade guide models, represents an advance when it comes to computer modeling in dentistry.
设计能够充分代表人类牙龈颜色的比色和光谱牙龈比色板模型。
使用先前编译的健康角化牙龈光谱反射数据库进行优化。采用 CIELAB 和 CIEDE2000 色差公式优化覆盖误差（CE）和最大误差（ME）。使用 FCM 算法和非线性优化进行两阶段处理。采用 t 检验比较不同数量的牙龈比色板模型（α=0.05）的聚类/色标性能。
CE 和 ME 在 3 到 6 个聚类的比色板模型中分别为 3.1 到 3.9（3 与 4 比较，P=0.028；5 与 6 比较，P=0.033），而相应的 CIEDE2000 范围为 2.1 到 2.8（3 与 4 色标比较，P<0.001；4 与 5 比较，P<0.025；5 与 6 比较，P=0.029）。CIELAB 色差低于可接受阈值的数据点百分比为 48.7%至 71.4%，CIEDE2000 为 52.9%至 82.4%。
牙龈比色板模型中聚类数的增加与对人类牙龈的覆盖误差（更好的匹配）降低有关。仅 4 个色标的牙龈比色板模型提供的 CIELAB 和 CIEDE2000 覆盖误差低于牙龈颜色的可接受阈值。人类牙龈的光谱聚类被证明是有效的。CIEDE2000 色差公式在优化过程中优于 CIELAB 公式。
提供具有较少色标且覆盖误差低于 50:50%可接受阈值的比色板模型将是牙科比色匹配的最佳解决方案。然而，实际上没有任何牙龈或牙齿比色板符合这一要求。聚类方法、覆盖误差和最大误差的优化以及光谱聚类，使比色板模型的聚类代表的颜色更可靠，这代表了牙科计算机建模的一个进步。
