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A spectral-discrimination method for tear-film lipid-layer thickness estimation from fringe pattern images

机译:一种基于条纹图案图像的泪膜脂质层厚度估计的光谱鉴别方法

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摘要

Examination of the tear-film lipid layer is often helpful in the prognosis of prospective contact lens patients and contact lens related problems, and in the analysis of symptomatic noncontact lens-wearing patients. In particular, the thickness of the lipid layer is considered to be an informative cue in studying the tear-film stability and uncovering of certain disorders. The authors propose a method for the accurate estimation of the lipid-layer thickness, exploiting the intensity and color information in Fizeau fringe images. The technique is based on a quantitative measure for discriminating among the spectra associated with different thicknesses. The authors propose an optical system for imaging the interference patterns, develop a mathematical model based on the physics of the fringe formation and sensing, and describe the calibration of the optical system using this model. The thickness extraction is readily carried out utilizing a lookup table. The proposed method would enable objective evaluation of the lipid layer characteristics, and provide a means for examining the dynamic changes in its thickness and spatial distribution during inter-blink periods.
机译:泪膜脂质层的检查通常有助于潜在的隐形眼镜患者的预后以及与隐形眼镜相关的问题,以及对有症状的非隐形眼镜佩戴者的分析。特别地,脂质层的厚度被认为是研究泪膜稳定性和发现某些疾病的有益信息。作者提出了一种利用Fizeau条纹图像中的强度和颜色信息准确估计脂质层厚度的方法。该技术基于定量方法,用于区分与不同厚度相关的光谱。作者提出了一种用于成像干涉图案的光学系统,基于条纹形成和传感的物理原理开发了数学模型,并描述了使用该模型对光学系统进行的校准。利用查找表很容易进行厚度提取。所提出的方法将能够客观地评估脂质层的特性,并提供一种方法来检查眨眼间期间其厚度和空间分布的动态变化。

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