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International Journal of Biomedical Imaging
Volume 2017 (2017), Article ID 4826385, 19 pages
Research Article

An Automatic Image Processing System for Glaucoma Screening

1Kellogg Eye Center, University of Michigan, 1000 Wall St, Ann Arbor, MI 48105, USA
2King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, National Guard, Riyadh 14611, Saudi Arabia
3Bin Rushed Ophthalmic Center, King Fahd Branch Rd, Opposite King Fahad National Library, Al Olaya, Riyadh 12311, Saudi Arabia
4Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria St., Toronto, ON, Canada M5B 2K3
5School of Optometry and Vision Science, University of Waterloo, 200 Columbia St. W., Waterloo, ON, Canada N2L 3G1

Correspondence should be addressed to Ahmed Almazroa

Received 23 October 2016; Revised 15 May 2017; Accepted 13 June 2017; Published 29 August 2017

Academic Editor: Guowei Wei

Copyright © 2017 Ahmed Almazroa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma’s population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm’s accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm’s accuracy. The algorithm’s best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images.