An automated and multi-parametric algorithm for objective analysis of meibography images

Meibography is a non-contact imaging technique used by ophthalmologists to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). While artificial qualitative analysis of meibography images could lead to low repeatability and efficiency and multi-parametric analysis is demanding to offer more comprehensive information in discovering subtle changes of meibomian glands during MGD progression, we developed an automated and multi-parametric algorithm for objective and quantitative analysis of meibography images. The full architecture of the algorithm can be divided into three steps: (1) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (2) segmentation and identification of glands within the ROI; and (3) quantitative multi-parametric analysis including newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and glands signal index (SI). To evaluate the performance of the automated algorithm, the similarity index (k) and the segmentation error including the false positive rate (r_P) and the false negative rate (r_N) are calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images. The feasibility of the algorithm is demonstrated in analyzing typical meibograhy images.

[1]  Kenji Inoue,et al.  Noncontact infrared meibography to document age-related changes of the meibomian glands in a normal population. , 2008, Ophthalmology.

[2]  K. Tsubota,et al.  Ocular surface changes and discomfort in patients with meibomian gland dysfunction. , 1995, Archives of ophthalmology.

[3]  B. Munoz,et al.  Prevalence of dry eye among the elderly. , 1997, American journal of ophthalmology.

[4]  James P McCulley,et al.  Meibomian gland function and the tear lipid layer. , 2003, The ocular surface.

[5]  Heiko Pult,et al.  Comparison of subjective grading and objective assessment in meibography. , 2013, Contact lens & anterior eye : the journal of the British Contact Lens Association.

[6]  S. Amano.  Meibomian Gland Dysfunction: Recent Progress Worldwide and in Japan. , 2018, Investigative ophthalmology & visual science.

[7]  Ayumu Furuta,et al.  Proposed diagnostic criteria for obstructive meibomian gland dysfunction. , 2009, Ophthalmology.

[8]  Jason J Nichols,et al.  An Assessment of Grading Scales for Meibography Images , 2005, Cornea.

[9]  B Seitz,et al.  [Meibomian glands : part III. Dysfunction - argument for a discrete disease entity and as an important cause of dry eye]. , 2009, Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft.

[10]  D. R. Iskander,et al.  A Novel Automated Approach for Infrared-Based Assessment of Meibomian Gland Morphology , 2019, Translational vision science & technology.

[11]  J. McCulley,et al.  Bacterial lipases and chronic blepharitis. , 1986, Investigative ophthalmology & visual science.

[12]  K. Tsubota,et al.  The application of in vivo laser confocal microscopy to the diagnosis and evaluation of meibomian gland dysfunction , 2008, Molecular Vision.

[13]  B. Bozkurt,et al.  Novel Application Software for the Semi-Automated Analysis of Infrared Meibography Images. , 2019, Cornea.

[14]  Heiko Pult,et al.  Non-contact meibography: keep it simple but effective. , 2011, Contact lens & anterior eye : the journal of the British Contact Lens Association.

[15]  Tae Joong Eom,et al.  In Vivo 3D Meibography of the Human Eyelid Using Real Time Imaging Fourier-Domain OCT , 2013, PloS one.

[16]  J. Paugh,et al.  Prevalence of Meibomian Gland Dysfunction , 1990, Optometry and vision science : official publication of the American Academy of Optometry.

[17]  F. Viola,et al.  In vivo confocal microscopy of meibomian glands in contact lens wearers. , 2011, Investigative ophthalmology & visual science.

[18]  Murat Dogru,et al.  The international workshop on meibomian gland dysfunction: report of the clinical trials subcommittee. , 2011, Investigative ophthalmology & visual science.

[19]  Robert Koprowski,et al.  A quantitative method for assessing the quality of meibomian glands , 2016, Comput. Biol. Medicine.

[20]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[21]  J. Buring,et al.  Prevalence of dry eye syndrome among US women. , 2003, American journal of ophthalmology.

[22]  Yang Wei Koh,et al.  Detection of meibomian glands and classification of meibography images. , 2012, Journal of Biomedical Optics.

[23]  D. Gaasterland,et al.  Investigative Ophthalmology & Visual Science , 1978 .

[24]  Hwee Kuan Lee,et al.  Bioimage Informatics Approach to Automated Meibomian Gland Analysis in Infrared Images of Meibography , 2013 .

[25]  Chia-Yen Lee,et al.  Fully automated lesion segmentation and visualization in automated whole breast ultrasound (ABUS) images. , 2020, Quantitative imaging in medicine and surgery.

[26]  Jun Shimazaki,et al.  The international workshop on meibomian gland dysfunction: report of the definition and classification subcommittee. , 2011, Investigative ophthalmology & visual science.

[27]  Jason J. Nichols,et al.  A Review of Meibography , 2012, Optometry and vision science : official publication of the American Academy of Optometry.

[28]  Reiko Arita,et al.  Objective image analysis of the meibomian gland area , 2013, British Journal of Ophthalmology.

[29]  Jung-Hyun Kim,et al.  Evaluation of automated and semi-automated skull-stripping algorithms using similarity index and segmentation error , 2003, Comput. Biol. Medicine.

[30]  T. Millar,et al.  The international workshop on meibomian gland dysfunction: report of the subcommittee on anatomy, physiology, and pathophysiology of the meibomian gland. , 2011, Investigative ophthalmology & visual science.

[31]  J. Larke,et al.  Meibomian gland dysfunction: some clinical, biochemical and physical observations , 1990, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[32]  Y Jie,et al.  Prevalence of dry eye among adult Chinese in the Beijing Eye Study , 2009, Eye.

[33]  Torsten Rohlfing,et al.  Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains , 2004, NeuroImage.

[34]  Chang Won Park,et al.  Novel Noncontact Meibography With Anterior Segment Optical Coherence Tomography: Hosik Meibography , 2012, Cornea.

[35]  Luigina Sorbara,et al.  Infrared Imaging of Meibomian Gland Structure Using a Novel Keratograph , 2012, Optometry and vision science : official publication of the American Academy of Optometry.