Koprowski Robert, Wilczyński Sławomir, Olczyk Paweł, Nowińska Anna, Węglarz Beata, Wylęgała Edward
Department of Biomedical Computer Systems, University of Silesia, Faculty of Computer Science and Materials Science, Institute of Computer Science, ul. Będzińska 39, Sosnowiec 41-200, Poland.
School of Pharmacy, Medical University of Silesia in Katowice, Poland.
Comput Biol Med. 2016 Aug 1;75:130-8. doi: 10.1016/j.compbiomed.2016.06.001. Epub 2016 Jun 2.
Meibomian gland dysfunction is a common cause of dry eye syndrome which can also lead to eyelid inflammation. Today, diagnostics of meibomian glands is not fully automatic yet and is based on a qualitative assessment made by an ophthalmologist. Therefore, this article proposes a new automatic analysis method which provides a quantitative assessment of meibomian gland dysfunction.
The new algorithm involves a sequence of operations: image acquisition (acquisition of data from OCULUS Keratograph® 5M); image pre-processing (image conversion to gray levels, median filtering, removal of uneven lighting, normalization); main image processing (binarization, morphological opening, labeling, Gaussian filtering, skeletonization, distance transform, watersheds). The algorithm was implemented in Matlab with Image Processing Toolbox (Matlab: Version 7.11.0.584, R2010b) on a PC running Windows 7 Professional, 64-bit with the Intel Core i7-4960X CPU @ 3.60GHz.
The algorithm described in this article has the following features: it is fully automatic, provides fully reproducible results - sensitivity of 99.3% and specificity of 97.5% in the diagnosis of meibomian glands, and is insensitive to parameter changes. The time of image analysis for a single subject does not exceed 0.5s. Currently, the presented algorithm is tested in the Railway Hospital in Katowice, Poland.
睑板腺功能障碍是干眼综合征的常见病因，也可导致眼睑炎症。目前，睑板腺的诊断尚未完全自动化，而是基于眼科医生的定性评估。因此，本文提出了一种新的自动分析方法，可对睑板腺功能障碍进行定量评估。
新算法包括一系列操作：图像采集（从OCULUS Keratograph® 5M获取数据）；图像预处理（转换为灰度图像、中值滤波、去除不均匀照明、归一化）；主要图像处理（二值化、形态学开运算、标记、高斯滤波、骨架化、距离变换、分水岭分割）。该算法在装有Windows 7 Professional 64位操作系统、英特尔酷睿i7 - 4960X CPU @ 3.60GHz的个人电脑上，使用Matlab图像处理工具箱（Matlab：版本7.11.0.584，R2010b）实现。
本文所述算法具有以下特点：完全自动化，结果完全可重复——在睑板腺诊断中灵敏度为99.3%，特异性为97.5%，且对参数变化不敏感。单个受试者的图像分析时间不超过0.5秒。目前，该算法正在波兰卡托维兹的铁路医院进行测试。
