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A new quantitative technique for grading Farnsworth D-15 color panel tests

机译:一种用于对Farnsworth D-15彩色面板测试进行分级的新定量技术

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There are three variables involved in modeling measurement errors - type, severity, and selectivity. Whereas clinicians typically utilize a graphical technique to grade color deficiencies based on D-15 panel tests, Vingrys and King-Smith developed a quantitative scoring technique for panel color tests, which models all three factors by utilizing an overall moment of inertia for color difference vectors (CDVs) calculated in 1976 CIELUV space. We propose a least squares analysis via linear regression of the errors (ΔU, ΔV) according to the following equation: ΔV=mΔU, where m=slope of best-fit line determined by linear regression. Error type is determined by the angular proximity of the best-fit line to known confusion axes representing protan, deutan, tritan or unspecified color defects. The severity is the sum of the CDV lengths of all errors made, and the selectivity is determined by the adjusted variance of the least squares fit. We determined normative threshold values for type, severity and selectivity by inspecting 142 cap arrangements with tentative diagnoses. We then analyzed 49 standard D-15 cap arrangements of subjects with definitive diagnoses to determine the sensitivity and specificity of our method to defect types. The results were then compared with those of Vingrys and King-Smith. Our linear regression technique provides an improved assessment of error arrangements that represent subtle unspecified color defects. However, our model appears too sensitive to atypical repositioning errors made when the majority of errors lie along known confusions. Used in conjunction with previous quantitative methods, linear regression by least squares proves a useful tool in the classification of errors of D-15 color panel tests.
机译:对测量误差进行建模涉及三个变量-类型,严重性和选择性。尽管临床医生通常根据D-15面板测试利用图形技术对色缺陷进行分级,但Vingrys和King-Smith开发了一种针对面板色彩测试的定量评分技术,该技术通过利用色差矢量的整体惯性矩对所有三个因素进行建模(CDV)是在1976年CIELUV空间中计算的。我们根据以下等式通过误差(ΔU,ΔV)的线性回归来提出最小二乘分析:ΔV=mΔU,其中m =通过线性回归确定的最佳拟合线的斜率。误差类型由最佳拟合线与代表毛边,氘代,三边或未指定颜色缺陷的已知混淆轴的角度接近度来确定。严重程度是所有错误的CDV长度的总和,而选择性则由最小二乘拟合的调整方差确定。我们通过对142个帽盖布置进行初步诊断,确定了类型,严重性和选择性的标准阈值。然后,我们用确定的诊断方法分析了49个标准的D-15帽盖安排,以确定我们的方法对缺陷类型的敏感性和特异性。然后将结果与Vingrys和King-Smith的结果进行比较。我们的线性回归技术可以更好地评估代表细微未指定颜色缺陷的错误排列。但是,当大多数错误都位于已知的混乱中时,我们的模型对于非典型的重新定位错误似乎过于敏感。与先前的定量方法结合使用,最小二乘线性回归证明是分类D-15彩色面板测试错误的有用工具。

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