Techniques of Glaucoma Detection From Color Fundus Images: A Review

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Author(s)

Malaya Kumar Nath 1,* Samarendra Dandapat 1

1. Department of Electronics and Electrical Engineering, IIT Guwahati

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2012.09.07

Received: 17 May 2012 / Revised: 5 Jul. 2012 / Accepted: 16 Aug. 2012 / Published: 8 Sep. 2012

Index Terms

Glaucoma, Optic neuropathy, Intraocular pressure, Cup area to Disc area ratio

Abstract

Glaucoma is a generic name for a group of diseases which causes progressive optic neuropathy and vision loss due to degeneration of the optic nerves. Optic nerve cells act as transducer and convert light signal entered into the eye to electrical signal for visual processing in the brain. The main risk factors of glaucoma are elevated intraocular pressure exerted by aqueous humour, family history of glaucoma (hereditary) and diabetes. It causes damages to the eye, whether intraocular pressure is high, normal or below normal. It causes the peripheral vision loss. There are different types of glaucoma. Some glaucoma occurs suddenly. So, detection of glaucoma is essential for minimizing the vision loss. Increased cup area to disc area ratio is the significant change during glaucoma. Diagnosis of glaucoma is based on measurement of intraocular pressure by tonometry, visual field examination by perimetry and measurement of cup area to disc area ratio from the color fundus images. In this paper the different signal processing techniques are discussed for detection and classification of glaucoma.

Cite This Paper

Malaya Kumar Nath,Samarendra Dandapat,"Techniques of Glaucoma Detection From Color Fundus Images: A Review", IJIGSP, vol.4, no.9, pp.44-51, 2012. DOI: 10.5815/ijigsp.2012.09.07

Reference

[1]P. N. Schacknow, J. R. Samples. The Glaucoma Book (First Edition). Portland, USA:Springer Publication, 2010.

[2]M. T. Leite, L. M. Sakata, F. A. Medeiros. Managing Glaucoma in Developing Countries. Arq Bras Oftalmol., 2011(74):83-84.

[3]B. Thylefors, A. D. Negrel. The Global Impact of Glaucoma. Bulletin of the World Health Organization, 1994(72):323-326.

[4]T. Y. Wong, S. C. Loon, S. M. Saw. The Epidemiology of Age Related Eye Diseases in Asia. British Journal of Ophthalmology, 2006(90):506-511.

[5]E. M. Hoffmann, L. M. Zangwill, J. G. Crowston, R. N. Weinreb. Optic Disk Size and Glaucoma. NIH Public Access, 2007(52):32-49.

[6]http://www.optic-disc.org

[7]http://www.willsglaucoma.org

[8]L. Xu, Y. Wang, J. Li. Single Intraocular Pressure Measurement for Glaucoma Detection. Beijing eye study, Acta Ophthalmologica, 2008(86).

[9]R. A. Abdel. Ghafar, T. Morris, T. Ritchings, I. Wood. Detection and Characterisation of the Optic Disc in Glaucoma and Diabetic Retinopathy. Annual Conference in Medical Image Understand (London, UK), 2004:23-24.

[10]http://www.moorfields.nhs.uk/

[11]J. Gloster, D. G. Parry. Use of Photographs for Measuring Cupping in the Optic Disc. British Journal of Ophthalmology, 1974(58):850-862.

[12]J. Funk, T. Dieringer, F. Grehn. Correlation Between Neuroretinal Rim Area and Age in Normal Subjects. Graefe’s Archive for Clinical and Experimental Ophthalmology, Springer Verlag, 1989(227):544-548.

[13]Barbara E. K. Klein, S. E. Moss, R. Klein, Y. L. Magli, C. H. Hoyer. Neuroretinal Rim Area in Diabetes Mellitus. Investigative Ophthalmology and Visual Science, 1990(31):805-809.

[14]E. Corona, S. Mitra, M. Wilson, T. Krile, Y. H. Kwon, P. Soliz. Digital Stereo Image Analyzer for Generating Automated 3-D Measures of optic Disc Deformation in Glaucoma. IEEE Transactions on Medical Imaging, 2002(21):1244-1253.

[15]A. R. McIntyre, M. I. Heywood, P. H. Artes, S. S. R. Abidi. Toward Glaucoma Classification With Moment Methods. Proceedings of the First Canadian (IEEE) Conference on Computer and Robot Vision (CRV04), 2004:265-272.

[16]S.-H. Meng, A. Turpin, M. Lazarescu, J. Ivins. Classifying Visual Field Loss in Glaucoma Through Baseline Matching of Stable Reference Sequences. IEEE International Conference on Machine Learning and Cybernetics, 2005:3686-3691.

[17]V. Pueyo, J. M. Larrosa, V. Polo, A. Perez-Inigo, A. Ferreras, F. M. Honrubia. Sector-Based Analysis of the Distribution of The Neuroretinal Rim by Confocal Scanning Laser in the Diagnosis of Glucoma. Arch Soc Esp Oftalmol, 2006(81):135-140.

[18]Y. Hayashi, T. Nakagawaa, Y. Hatanakac, A. Aoyamaa, M. Kakogawab, T. Haraa, H. Fujitaa, T. Yamamotoa. Detection of Retinal Nerve Fiber Layer Defects in Retinal Fundus Images Using Gabor Filtering. Medical Imaging:SPIE, 2007(6514):65142Z1-65142Z8.

[19]F. Fink, K. Wrle, P. Gruber, A. M. Tom, J. M. G. Sez, C. G. Puntonet, E. W. Lang. ICA Analysis of Retinal Images for Glaucoma Classification. IEEE Conference, Vancouver, British Columbia, Canada, 2008:4664-4667.

[20]J. Nayak, Rajendra Acharya U., P. S. Bhat, N. Shetty, T.-C. Lim. Automated Diagnosis of Glaucoma Using Digital Fundus Images. Journal of Medical Systems, Springer, 2009(33):337-346.

[21]J. Miguel-Jimenez, S. Ortega, L. Boquete, J. M. Rodriguez-Ascariz, R. Blanco. Multifocal Electroretinography: Structural Pattern Analysis and Early Glaucoma Detection. Electronics Letters, 2009(45):1113-1115.

[22]Y. Hatanaka, A. Noudo, C. Muramatsu, A. Sawada, T. Hara, T. Yamamoto, H. Fujita. Automatic Measurement of Cup to Disc Ratio Based on Line Profile Analysis in Retinal Images. 33RD Annual International Conference of the IEEE EMBS (IEEE, ed.), (Boston, Massachusetts USA), 2011:3387-3390.

[23]Y. Xu, D. Xu, S. Lin, J. Liu, J. Cheng, C. Y. Cheung, T. Aung, T. Y. Wong. Sliding Window and Regression Based Cup Detection in Digital Fundus Images for Glaucoma Diagnosis. Springer-Verlag, 2011:1-8.

[24]Z. Zhang, C. K. Kwoh, J. Liu, F. Yin, A. Wirawan, C. Cheung, M. Baskaran, T. Aung, T. Y. Wong. MRMR Optimized Classification for Automatic Glaucoma Diagnosis. 33RD Annual International Conference of the IEEE EMBS, (Boston, Massachusetts USA), 2011:6228-6231.

[25]M. Mishra, M. K. Nath, S. R. Nirmala, S. Dandapat. Image Processing Techniques for Glaucoma Detection. Communications in Computer and Information Science: Advances in Computing and Communications: Springer, 2011(192):365-373.

[26]Malaya Kumar Nath, M. Mishra, and S. Dandapat. PCA and LDA Based Approach to Glaucoma Classification from Color Fundus Images. 35TH National Systems Conference, (IIT Bhubaneswar, India), 2011:186-191.

[27]R. Bock, J. Meier, L. G. Nyul, J. Hornegger, G. Michelson. Glaucoma Risk Index: Automated Glaucoma Detection from Color Fundus Images. Elsevier: Medical Image Analysis, 2010(14):471-481.

[28]G. D. Joshi, J. Sivaswamy, S. R. Krishnadas. Optic Disk and Cup Segmentation from Monocular Colour Retinal Images for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 2011(30):1192-1205.

[29]S. Dua, U. R. Acharya, P. Chowriappa, S. V. Sree. Wavelet-Based Energy Features for Glaucomatous Image Classification. IEEE Transactions on Information Technology in Biomedicine, 2012(16):80-87.

[30]Z. Zhang, F. S. Yin, J. Liu, W. K. Wong, N. M. Tan, B. H. Lee, J. Cheng, T. Y. Wong. ORIGAlight: An Online Retinal Fundus Image Database for Glaucoma Analysis and Research. 32ND Annual IEEE International Conference of the EMBS, Buenos Aires, Argentina, 2010:3065-3068.

[31]F. Fumero, S. Alayon, J. Sanchez, J. Sigut, M. Gonzalez-Hernandez. RIM-ONE: An Open Retinal Image Database for Optic Nerve Evaluation. 24TH IEEE International Symposium on Computer-Based Medical Systems (CBMS), Bristol, 2011:1–6.