Purpose: We present and validate a new methodology for analyzing, in an automated and objective fashion, infrared images of the meibomian glands (MG).
Methods: The developed algorithm consists of three main steps: selection of the region of interest, detection of MG, and analysis of MG morphometric parameters and dropout area (DOA). Additionally, a new approach to quantify the irregularity of MG is introduced. We recruited 149 adults from a general population. Infrared meibography, using Keratograph 5M, was performed. Images were assessed and graded subjectively (Meiboscore) by two experienced clinicians and objectively with the proposed automated method.
Results: The correlation of subjective DOA assessment between the two clinicians was poor and the average percentage of DOA estimated objectively for each Meiboscore group did not lie within their limits. The objective assessment showed lower variability of meibography grading than that obtained subjectively. Additionally, a new grading scale of MG DOA that reduces intraclass variation is proposed. Reported values of MG length and width were inversely proportional to the DOA. Gland irregularity was objectively quantified.
Conclusions: The proposed automatic and objective method provides accurate estimates of the DOA as well as additional morphologic parameters that could add valuable information in MG dysfunction understanding and diagnosis.
Translational Relevance: This approach highlights the shortcomings of currently used subjective methods, and provides the clinicians with an objective, quantitative and less variable alternative for assessing MG in a noninvasive and automated fashion. It provides a viable alternative to more time-consuming subjective methods.

A Novel Automated Approach for Infrared-Based Assessment of Meibomian Gland Morphology.

Meibomian gland dysfunction (MGD) is one of the most common diseases observed in clinics and is the leading cause of evaporative dry eye. Today, diagnostics of MGD is not fully automatic yet and is based on a qualitative assessment made by an ophthalmologist. Therefore, an automatic analysis method was developed to assess MGD quantiatively. The analysis made use of 228 images of 57 patients recorded by OCULUS Keratograph® 5 M with a resolution of 1024 × 1360 pixels concern 30 eyes of healthy individuals (14 women and 16 men) and 27 eyes of sick patients (10 women and 17 men). The diagnosis of dry eye was made according to the consensus of DED in China (2013). The presented method of analysis is a new, developed method enabling an automatic, reproducible and quantitative assessment of Meibomian glands. The analysis relates to employing the methods of analysis and image processing. The analysis was conducted in the Matlab environment Version 7.11.0.584, R2010b, Java VM Version: Java 1.6.0_17-b04 with Sun Microsystems Inc. with toolboxes: Statistical, Signal Processing and Image Processing. The presented, new method of analysis of Meibomian glands is fully automatic, does not require operator’s intervention, allows obtaining reproducible results and enables a quantitative assessment of Meibomian glands. Compared to the other known methods, particularly with the method described in literature it allows obtaining better sensitivity (98%) and specificity (100%) results by 2%.

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A clinical utility assessment of the automatic measurement method of the quality of Meibomian glands.

Background Meibography is a non-contact imaging technique used by ophthalmologists and eye care practitioners to acquire information on the characteristics of meibomian glands. One of its most important applications is to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). As the artificial qualitative analysis of meibography images can lead to low repeatability and efficiency, automated and quantitative evaluation would greatly benefit the image analysis process. Moreover, since the morphology and function of meibomian glands varies at different stages of MGD, multiparametric analysis offering more comprehensive information could help in discovering subtle changes to glands during MGD progression. Therefore, an automated and multiparametric objective analysis of meibography images is urgently needed. Methods An algorithm was developed to perform multiparametric analysis of meibography images with fully automatic and repeatable segmentation based on image contrast enhancement and noise reduction. The full architecture can be divided into three steps: (I) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (II) segmentation and identification of glands within the ROI; and (III) quantitative multiparametric analysis including a newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). To evaluate the performance of this automated algorithm, the similarity index (k) and the segmentation error including the false-positive rate (rP ) and the false-negative rate (rN ) were calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images. Results The results of the performance evaluation between the manually defined ground truth and automatic segmentations were as follows: for ROI segmentation, the similarity index (k)=0.94±0.02, the false-positive rate (rP )=6.02%±2.41%, and the false-negative rate (rN )=6.43%±1.98%; for meibomian gland segmentation, the similarity index (k)=0.87±0.01, the false-positive rate (rP )=4.35%±1.50%, and the-false negative rate (rN )=18.61%±1.54%. The algorithm was successfully applied to process typical meibography images acquired from subjects of different meibomian gland health statuses, by providing the gland area ratio (GA), the gland length (L), gland width (D), gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). Conclusions A fully automated algorithm was developed which demonstrated high similarity with moderate segmentation errors for meibography image segmentation compared with the manual approach, offering multiple parameters to quantify the morphology and function of meibomian glands for the objective evaluation of meibography images.

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An automated and multiparametric algorithm for objective analysis of meibography images

Comparison of meibomian gland dropout using two infrared imaging devices

The meibomian glands are lipid-secreting glands located in the tarsal plates, whose secretory products cover the tear film, thereby reducing evaporation as well as ensuring lubrication of the ocular surface. The meibomian glands can be visualized at different levels of magnification by infrared meibography, laser confocal microscopy, and optical coherence tomography. These imaging modalities have been subject to much research and progress in clinical practice and have shaped our current understanding of meibomian glands in health and disease. In this review, we explore the evolution of meibography over the past decades, the major contributions of various meibographic modalities, and discuss their clinical significance.

Edward Wylźgała

Papers

A quantitative method for assessing the quality of meibomian glands