管道(软件)
颜色校正
计算机科学
色彩平衡
人工智能
计算机视觉
色差
色彩管理
亮度
颜色直方图
直方图
颜色量化
色空间
比色法
颜色归一化
彩色图像
图像处理
光学
图像(数学)
GSM演进的增强数据速率
物理
程序设计语言
作者
Nidhi Menon,David Beery,Prava Sharma,Adrian Crutchfield,L Kim,Aaron Lauer,Ayesha Azimuddin,Brianna Wronko-Stevens
出处
期刊:PLOS ONE
[Public Library of Science]
日期:2024-10-04
卷期号:19 (10): e0311343-e0311343
标识
DOI:10.1371/journal.pone.0311343
摘要
Color correction is an important methodology where a digital image’s colors undergo a transformation to more accurately represent their appearance using a predefined set of illumination conditions. Colorimetric measurements in diagnostics are sensitive to very small changes in colors and therefore require consistent, reproducible illumination conditions to produce accurate results, making color correction a necessity. This paper presents an image color correction pipeline developed by HueDx, Inc., using transfer algorithms that improve upon existing methodologies and demonstrates real-world applications of this pipeline in colorimetric clinical chemistry using a smartphone enabled, paper-based total protein diagnostic assay. Our pipeline is able to compensate for a variety of illumination conditions to provide consistent imaging for quantitative colorimetric measurements using white-balancing, multivariate gaussian distributions and histogram regression via dynamic, non-linear interpolating lookup tables. We empirically demonstrate that each point in the color correction pipeline provides a theoretical basis for achieving consistent and precise color correction. To show this, we measure color difference with deltaE (ΔE00), alongside quantifying performance of the HueDx color correction system, including the phone hardware, color sticker manufacturing quality and software correction capabilities. The results show that the HueDx color correction system is capable of restoring images to near-imperceptible levels of difference independent of their original illumination conditions including brightness and color temperature. Comparisons drawn from the paper-based total protein assay calibrated and quantified with and without using the HueDx color correction pipeline show that the coefficient of variation in precision testing is almost twice as high without color-correcting. Limits of blank, detection and quantitation were also higher without color-correction. Overall, we were able to demonstrate the HueDx platform improves reading and outcome of the total protein diagnostic assay and is useful for the development of smartphone-based quantitative colorimetric diagnostic assays for point-of-care testing.
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