In recent years, significant progress has been made in the Meibography technique resulting from the use of advanced image analysis methods allowing a quantitative description of the Meibomian gland structures. Many objective measures of gland distortion were previously proposed allowing for user-independent classification of acquired gland images. However, due to the complicated nature of gland deformation, none of the single-valued parameters can fully describe the analyzed gland images. There is a need to increase the number of descriptive factors, selectively sensitive to different gland features. Here we show that global 2D Fourier transform analysis of infra-red gland images provides values of two new such parameters: mean gland frequency and anisotropy in gland periodicity. We show that their values correlate with gland dysfunction and can be used to automatically categorize the images into the three subjective classes (healthy, intermediate and unhealthy). We also demonstrated that classification performance can be improved by dimensionality reduction approach using principal component analysis.

近年来，由于使用了先进的图像分析方法，可以对睑板腺结构进行定量描述，从而使睑板成像技术取得了重大进展。先前提出了许多客观的腺体变形测量方法，以允许用户独立地对采集的腺体图像进行分类。但是，由于腺体变形的复杂性，单值参数都无法完全描述所分析的腺体图像。需要增加选择性地对不同腺体特征敏感的描述性因素的数量。在这里，我们显示了对红外线腺体图像的全局2D傅里叶变换分析提供了两个新的此类参数的值：平均腺体频率和腺体周期性中的各向异性。我们表明，它们的值与腺体功能障碍相关，可用于将图像自动分类为三个主观类别（健康，中级和不健康）。我们还证明了通过使用主成分分析的降维方法可以提高分类性能。