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Journal of Healthcare Engineering
Volume 2017, Article ID 5953621, 14 pages
Research Article

Automatic CDR Estimation for Early Glaucoma Diagnosis

1Biomedical Engineering and Telemedicine Research Group, University of Cádiz, Puerto Real, Cádiz, Spain
2Signal Theory and Communication Department, University of Seville, Seville, Spain
3Ophthalmology Unit, Puerta del Mar Hospital, Cádiz, Spain

Correspondence should be addressed to M. A. Fernandez-Granero; and I. Fondón;

Received 1 June 2017; Revised 9 September 2017; Accepted 24 September 2017; Published 27 November 2017

Academic Editor: Andreas Maier

Copyright © 2017 M. A. Fernandez-Granero 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.


Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE Lab colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE Lab values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.