A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images

Full Text (PDF, 587KB), PP.55-69

Views: 0 Downloads: 0


Sreejini K S 1,* V.K. Govindan 1

1. Department of Computer Science and Engineering, NIT Calicut, Kerala, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2015.11.08

Received: 10 Jun. 2015 / Revised: 24 Jul. 2015 / Accepted: 1 Sep. 2015 / Published: 8 Oct. 2015

Index Terms

Diabetic retinopathy, retina fundus image, optic disc, macula, fovea, blood vessels, exudates, microaneurysms, hemorrhages, segmentation, survey


Ophthalmology is the study of structures, functions, treatment and disorders of eye. Computer aided analysis of retina images is still an open research area. Numerous efforts have been made to automate the analysis of retina images. This paper presents a review of various existing research in detection of anatomical structures in retina and lesions for the diagnosis of diabetic retinopathy (DR). The research in detection of anatomical structures is further divided into subcategories, namely, vessel segmentation and vessel centerline extraction, optic disc segmentation and localization, and fovea/ macula detection and extraction. Various research works in each of the categories are reviewed highlighting the techniques employed and comparing the performance figures obtained. The issues/ lacuna of various approaches are brought out. The following major observations are made: Most of the vessel detection algorithms fail to extract small thin vessels having low contrast. It is difficult to detect vessels at regions where close vessels are merged, at regions of missing of small vessels, at optic disc regions, and at regions of pathology. Machine learning based approaches for blood vessel tracing requires long processing time. It is difficult to detect optic disc radius or boundary with simple blood vessel tracing. Automatic detection of fovea and macular region extraction becomes complicated due to non-uniform illuminations while imaging and diseases of the eyes. Techniques requiring prior knowledge leads to complexity. Most lesion detection algorithms underperform due to wide variations in the color of fundus images arising out of variations in the degree of pigmentation and presence of choroid.

Cite This Paper

Sreejini K S, V. K Govindan,"A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images", IJIGSP, vol.7, no.11, pp.55-69, 2015. DOI: 10.5815/ijigsp.2015.11.08


[1]Kanski J J. Clinical Ophthalmology: A Systemic Approach. 3rd ed. Oxford: Butterworth-Heinemann, 1994

[2]Scanlon, P. H., S. J. Aldington, and I. M. Stratton. Delay in diabetic retinopathy screening increases the rate of detection of referable diabetic retinopathy, Diabetic Medicine 31.4 (2014): 439-442.

[3]National Eye Institute, National Institute of Health, [online]: http://www.nei.nih.gov/health/ 

[4]DIARETDB1 - Standard Diabetic Retinopathy Database Calibration level 1. [Online]: http://www2.it.lut.fi/project/imageret/diaretdb1/index.html

[5]DRIVE dataset [Online]: http://www.isi.uu.nl/Research/Databases/DRIVE/

[6]STARE: Structured Analysis of the Retina, [Online]: http://www.ces.c1emson.edul-ahoover/stare/

[7]L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, K. W. Tobin and E. Chaum, Automatic retina exudates segmentation without a manually labelled training set, Proc. of the 8th IEEE Int. Symp. Biomed. Imag: From Nano to Macro, ISBI 2011, Chicago, USA, pp. 1396 –1400.

[8]MESSIDOR: Methods for evaluating segmentation and indexing techniques dedicated to retinal ophthalmology. [Online]. Available: http://messidor.crihan.fr/index-en.php

[9]Decencière, E., Cazuguel G., Zhang TeleOphta: Machine learning and image processing methods for teleophthalmology, IRBM 34.2 (2013): 196-203.

[10]The VICAVR database, http://www.varpa.es/vicavr.html, 2010.

[11]Deepak, K. Sai, and Jayanthi Sivaswamy. Automatic assessment of macular edema from color retinal images, Medical Imaging, IEEE Transactions on 31.3 (2012): 766-776.

[12]Sreejini K S and V K Govindan. Severity Grading of DME from Retina Images: A Combination of PSO and FCM with Bayes Classifier, International Journal of Computer Applications 81(16):11-17, November 2013

[13]H. Leung, J.J. Wang, E. Rochtchina, T.Y. Wong, R. Klein, P. Mitchell, Impact of current and past blood pressure on retinal arteriolar diameter in older population, Journal of Hypertension (22) (2004) 1543–1549.

[14]P. Mitchell, H. Leung, J.J. Wang, E. Rochtchina, A.J. Lee, T.Y. Wong, R. Klein, Retinal vessel diameter and open-angle glaucoma: the Blue Mountains eye study, Ophthalmology (112) (2005) 245–250

[15]J.J. Wang, B. Taylor, T.Y. Wong, B. Chua, E. Rochtchina, R. Klein, P. Mitchell, Retinal vessel diameters and obesity: a population-based study in older persons, Obesity Research (14) (2006) 206–214.

[16]Nguyen, Thanh Tan. Relationship of Retinal Vascular Caliber with Diabetes and Retinopathy The Multi-Ethnic Study of Atherosclerosis (MESA), Diabetes Care 31.3 (2008): 544-549.

[17]K. Akita, H. Kuga, A computer method of understanding ocular fundus images, Pattern Recognition 15 (1982) 431–443

[18]S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum, Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Transactions on Medical Imaging 8 (1989) 263–269.

[19]Zolfagharnasab and Naghsh-Nilchi: Cauchy based matched filter for retinal vessels detection, Journal of Medical Signals & Sensors, Vol 4, Issue 1, 2014.

[20]Zolfagharnasab H, Naghsh-Nilchi AR. Retinal vessels detection based on matched filter using student distribution function. Journal of Medical and Biomedical Engineering 2013; Manuscript No. JMBE 1745- Manuscript submitted for publication.

[21]Mohammed Al-Rawi, Munib Qutaishat, and Mohammed Arrar. An improved matched filter for blood vessel detection of digital retinal images. Computers in Biology and Medicine, 37(2):262-267, 2007.

[22]Mohammed Al-Rawi and Huda Karajeh. Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images. Computer Methods and Programs in Biomedicine, 87(3):248-253, 2007.

[23]M. G. Cinsdikici and D. Aydin. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Computer methods and programs in biomedicine, 96(2):85-95, 2009.

[24]Chanwimaluang, Thitiporn, and Guoliang Fan. An efficient blood vessel detection algorithm for retinal images using local entropy thresholding, Circuits and Systems, 2003. ISCAS'03. Proceedings of the 2003 International Symposium on. Vol. 5. IEEE, 2003.

[25]A. Hoover, V. Kouznetsova, and M. Goldbaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Trans. Med. Imag., vol. 19, pp. 203–210, Mar. 2000.

[26]B. Zhang, L. Zhang, L. Zhang, F. Karray, Retinal vessel extraction by matched filter with first-order derivative of Gaussian, Computers in Biology and Medicine 40 (2010) 438–445.

[27]C. Yao, H.-j. Chen, Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm, Journal of Central South University of Technology 16 (2009) 640–646.

[28]Sofka, Michal, and Charles V. Stewart. Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures, Medical Imaging, IEEE Transactions on 25.12 (2006): 1531-1546.

[29]Qin Li, Jane You, and David Zhang. Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Systems with Applications, 39(9):7600-7610, 2012.

[30]Bob Zhang, Fakhri Karray, Qin Li, Lei Zhang, Sparse Representation Classifier for microaneurysm detection and retinal blood vessel extraction, Information Sciences, 2012, pp. 78–90.

[31]A. M. Mendon?a and A. Campilho. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction, IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Sep. 2006.

[32]Amin, M. Ashraful Amin, Hong Yan, High speed detection of retinal blood vessels in fundus image using phase congruency, Soft Comput. (2011) 15:1217–1230.

[33]Vlachos, Marios, and Evangelos Dermatas. Multi-scale retinal vessel segmentation using line tracking, Computerized Medical Imaging and Graphics34.3 (2010): 213-227. 

[34]C. Sinthanayothin, J. Boyce, H. Cook, and T. Williamson. Automated localization of the optic disc, fovea, and retinal blood vessels from digital colour fundus images, British Journal of Ophthalmology, vol. 83, no. 11, pp. 902–910, 1999.

[35]J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. V. Ginneken. Ridge based vessel segmentation in color images of the retina, IEEE Trans. Med. Imag., vol. 23, no, pp: 501–509, Apr. 2004.

[36]X. Jiang and D. Mojon. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images, IEEE Transaction on pattern analysis and machine intelligence, Vol. 25, No. 1, January 2003.

[37]M.M. Fraz, P. Remagnino, A. Hoppe, Sergio Velastin, B. Uyyanonvara and S. A .Barman. A Supervised Method for Retinal Blood Vessel Segmentation Using Line Strength, Multiscale Gabor and Morphological Features, in ICSIPA 2011.

[38]M.M. Fraz, S.A. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, A.R. Rudnicka, C.G. Owen. An approach to localize the retinal blood vessels using bit planes and centerline detection, Computer methods and programs in biomedicine 108.2 (2012): 600-616.

[39]Diego Marín, Arturo Aquino, Manuel Emilio Gegúndez-Arias, and José Manuel Bravo, A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features, IEEE Transactions on Medical Imaging, 2010.

[40]Orlando, José Ignacio, and Matthew Blaschko. Learning fully-connected CRFs for blood vessel segmentation in retinal images, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer International Publishing, 2014. 634-641.

[41]Giri Babu Kande, T.Satya Savithri, and P.V.Subbaiah, Segmentation of Vessels in Fundus Images using Spatially Weighted Fuzzy c-Means Clustering Algorithm, International Journal of Computer Science and Network Security, VOL.7 No.12, December 2007

[42]Lam and Yan, A Novel Vessel Segmentation Algorithm for Pathological Retina Images, IEEE Transactions on Medical Imaging, Vol. 27, No. 2, February 2008.

[43]Saffarzadeh, Vahid Mohammadi, Alireza Osareh, and Bita Shadgar. Vessel segmentation in retinal images using multi-scale line operator and K-means clustering, Journal of medical signals and sensors 4.2 (2014): 122.

[44]Uyen T.V.Nguyen, Alauddin Bhuiyan, Laurence, A.F.Park, Kotagiri Ramamohanarao, An effective retinal blood vessel segmentation method using multi-scale line detection, Pattern Recognition 46 (2013) 703–715

[45]Lam, Benson SY, Yongsheng Gao, and AW-C. Liew. General retinal vessel segmentation using regularization-based multiconcavity modelling, Medical Imaging, IEEE Transactions on 29.7 (2010): 1369-1381.

[46]Ganjee, Razieh, Reza Azmi, and Behrouz Gholizadeh. An Improved Retinal Vessel Segmentation Method Based on High Level Features for Pathological Images, Journal of medical systems 38.9 (2014): 1-9.

[47]Osareh and Shadgar, Automatic Blood Vessel Segmentation in Color Images of Retina, Iranian Journal of Science & Technology, Transaction B, Engineering, Vol. 33, No. B2, pp 191-206, 2009.

[48]A. Fathi, A.R. Naghsh-Nilchi , Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation, Biomedical Signal Processing and Control 8 (2013) 71– 80

[49]Imani, Elaheh, Malihe Javidi, and Hamid-Reza Pourreza. Improvement of retinal blood vessel detection using morphological component analysis, Computer methods and programs in biomedicine 118.3 (2015): 263-279. 

[50]Wihandika, Randy Cahya, and Nanik Suciati. Retinal Blood Vessel Segmentation with Optic Disc Pixels Exclusion, International Journal of Image, Graphics and Signal Processing (IJIGSP) 5.7 (2013): 26. 

[51]Sakiko Teramoto, Kyoko Ohno-Matsui, Takashi Tokoro and Seiji Ohno. Bilateral Large Peripapillary Venous and Arterial Loops, Jpn J Ophthalmol 43, pp: 422–425 (1999).

[52]Franklin, S. Wilfred, and S. Edward Rajan. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images, Biocybernetics and Biomedical Engineering 34.2 (2014): 117-124.

[53]Keith A. Goatman, Alan D. Fleming, Sam Philip, Graeme J. Williams, John A. Olson, and Peter F. Sharp, Detection of New Vessels on the Optic Disc Using Retinal Photographs, IEEE Transactions on Medical Imaging, Vol. 30, No. 4, April 2011

[54]Dashtbozorg, Behdad, Ana Maria Mendon?a, and Aurélio Campilho. An automatic graph-based approach for artery/vein classification in retinal images, Image Processing, IEEE Transactions on 23.3 (2014): 1073-1083.

[55]Qazaleh Mirsharif, Farshad Tajeripour, Hamidreza pourreza. Automated classification of blood vessels as arteries and veins in retinal images, computerized Meical imaging and graphics, 37(2013), pp: 607-617.

[56]Grisan, Enrico, and Alfredo Ruggeri. A divide et impera strategy for automatic classification of retinal vessels into arteries and veins, Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE. Vol. 1. IEEE, 2003.

[57]W. Lotmar, A. Freiburghaus, D. Bracher, Measurement of vessel tortuosity on fundus photographs, GraeJe 's Arch. Clin. Exp. Ophthalmol., VoI.211,pp. 49-57, 1979.

[58]Heneghan C., Flynn J., Michael O'Keefe and Mark Cahill. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis, Medical image analysis 6.4 (2002): 407-429.

[59]Onkaew D, Turior R, Uyyanonvara B, Akinori N, Sinthanayothin C. Automatic retinal vessel tortuosity measurement using curvature of improved chain code. In Proceedings of the International Conference on Electrical, Control and Computer Engineering (In- ECCE 2011), pp 183–6.

[60]Vinayak Shivkumar Joshi, Michael D. Abramoff. Analysis of retinal vessel networks using quantitative descriptors of vascular morphology, (2012).

[61]Turior Rashmi, Danu Onkaew, Bunyarit Uyyanonvara, Pornthep Chutinantvarodom. Quantification and classification of retinal vessel tortuosity, Science Asia 39 (2013): 265-277.

[62]Trucco, Emanuele, Hind Azegrouz, and Baljean Dhillon. Modeling the tortuosity of retinal vessels: does caliber play a role?, Biomedical Engineering, IEEE Transactions on 57.9 (2010): 2239-2247.

[63]A. Aquino, M.E. Gegundez-Arias, and D. Marin. Detecting the optic disc boundary in digital fundus images using morphological, edge detection and feature extraction techniques, IEEE transactions on medical imaging, 29(10), pp: 1860-1869, 2010

[64]A. A. H. A. R. Youssif, A. Z. Ghalwash, and A. R. Ghoneim. Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter, IEEE Trans. Med. Imag., vol. 27, pp: 11–18, 2008

[65]A. Hoover and M. Goldbaum. Locating the optic nerve in retinal image using the fuzzy convergence of the blood vessels, IEEE Transactions on Medical Imaging, 22(8), pp: 951–958, 2003.

[66]Mahfouz A, Fahmy AS. Fast localization of the optic disc using projection of image features. IEEE Transactions on Image Processing 2010;19: 3285–9.

[67]Yu H, Barriga S, Agurto C, Echegaray S, Pattichis M, Zamora G, et al. Fast localization of optic disc and fovea in retinal images for eye disease screening. In: Proceedings of SPIE conference on medical imaging 2011: computer-aided diagnosis. 2011.

[68]Sinha, Neelam, and R. Venkatesh Babu. Optic disk localization using L1 minimization, Image Processing (ICIP), 2012 19th IEEE International Conference on. IEEE, 2012. 

[69]Chin K S, Trucco E, Tan L and Wilson P J. Automatic fovea location in retinal images using anatomical priors and vessel density, Pattern Recognition Letters 34.10 (2013): 1152-1158.

[70]R. Philips, J. Forrester, and P. Sharp. Automated detection and quantification of retinal exudates, Graefe's Arch. Clin. Exp. Ophthalmol., vol. 231, pp: 90–94, 1993

[71]Akara Sopharak, Bunyarit Uyyanonvara and Sarah Barman. Automatic Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Mathematical Morphology Methods, IAENG International Journal of Computer Science, 38: 3, August 2011.

[72]C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal and D. Usher. Automated detection of DR on digital fundus image, Journal of Diabet. Med., 19, pp: 105-112, 2002.

[73]Walter, T., Klein, J. C., Massin and P., Erginay. A contribution of image processing to the diagnosis of diabetic retinopathy-Detection of exudates in color fundus images of the human retina, IEEE Transactions on Medical Imaging,Volume 21, Issue 10, pp: 1236 – 1243, Oct. 2002.

[74]Akara Sopharak , Bunyarit Uyyanonvara, Sarah Barman and Tom Williamson. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods, Journal of Computerized Medical Imaging and Graphics, pp: 720 – 727, 2008.

[75]Ahmed wasif Reza, C. Eswaran and Kaharudin Dimyati, Diagnosis of Diabetic Retinopathy: Automatic Extraction of optic disc and exudates from retinal images using marker controlled watershed transformation, Springer, J Med. Syst., 2011, Vol. 35, pp. 1491-1501.

[76]L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, K. W. Tobin and E. Chaum, Automatic retina exudates segmentation without a manually labeling training set, In Proc. of IEEE International Symposium on Biomedical Imaging, 2011.

[77]S. T. Lim, W. M. D. W. Zaki, A. Hussain, S. L. Lim and S. Kusalavan. Automatic Classification of Diabetic Macular Edema in Digital Fundus Images, In CHUSER 2011, Dec, Penag, 2011.

[78]A. Osareh, M. Mirmehdi, B. Thomas and Richard Markham. Automatic recognition of exudative maculopathy using fuzzy c - means clustering and neural networks, Proceedings of Medical Image Understanding and Analysis, UK, pp: 49–52, July 2001.

[79]M. Niemeijer, B. van Ginneken, S R Russell, M. S. Suttorp-Schulten and M. D. Abràmoff. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis, Invest. Ophthalmol. Vis. Sci., Vol. 48, pp: 2260-7, May 2007.

[80]Guoliang Fang. Nan Yang, Huchuan Lu and Kaisong Li. Automatic segmentation of hard exudates in fundus images based boosted soft segmentation, in proc. Int. conference on intelligent control and information processing, pp: 633-638, August 2010.

[81]Akara Sopharak, Mathew N dailey, Bunyarit Uyyanonvara, Sarah Barman, Tom Williamson and Yin Aye Moe. Machine learning approach to automatic exudate detection in retinal image from diabetic patients, Journal of Modern optics, Vol. 57, No.2, pp: 124 – 135, Nov 2011.

[82]C. I. Sánchez, María García, Agustín Mayo, María I. López and Roberto Hornero. Retinal image analysis based on mixture models to detect hard exudate, Medical Image Analysis, Vol. 13, pp: 650–658, 2009.

[83]M. Garcia, C. I. Sanchez, M.I. Lopez, D. Abasolo and R. Hornero. Neural network based detection of hard exudates in retinal images, Computer methods and Programs in Biomedicine, vol. 93, pp. 9-19, 2009. 

[84]Sopharak, A, Nwe, K. T, Moe, Y. A, Dailey, M. N and Uyyanonvara B. Automatic exudate detection with a naive Bayes classifier, In International Conference on Embedded Systems and Intelligent Technology, pp: 139–142, 2008.

[85]Osareh A., Shadgar B. and Markham R. A Computational-Intelligence-Based Approach for Detection of exudates in Diabetic Retinopathy Images, IEEE transaction on Information Technology in biomedicine, Volume 13, Issue 4, pp: 535 – 545, July 2009

[86]G. Gardner, D. Keating, T. Williamson, and A. Elliot, Detection of diabetic retinopathy using neural network analysis of fundus images, British Journal of Ophthalmology, vol. 80, no. 11, pp: 937–948, 1996.

[87]Akara Sopharak, Bunyarit Uyyanonvara and Sarah Barman. Fine Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using a Hybrid Approach, Proceedings of the World Congress on Engineering, Vol II, 2012.

[88]Lee Streeter and Michael J. Cree. Microaneurysms detection in colour fundus images, Image and Vision Computing, 280 - 284, Nov-2003.

[89]Gwénolé Quellec, Mathieu Lamard, Pierre Marie Josselin, Guy Cazuguel, Béatrice Cochener, and Christian Roux. Optimal wavelet transform for the detection of microaneurysms in retina photographs, Medical Imaging, IEEE Transactions on 27.9 (2008): 1230-1241.

[90]Zhang, X., Thibault G., Decenciere, E., Lay B., Danno, R., Ali Ergginay. Exudate detection in color retinal images for mass screening of diabetic retinopathy, Medical image analysis 18.7 (2014): 1026-1043. 

[91]Harangi, Balazs, and Andras Hajdu. Automatic exudate detection by fusing multiple active contours and regionwise classification, Computers in biology and medicine 54 (2014): 156-171.

[92]Handayani Tjandrasa, Isye Arieshanti, Radityo Anggoro. Classification of non-proliferative diabetic retinopathy based on segmented exudates using K-Means clustering, I.J. Image, Graphics and Signal Processing 1 (2015): 1-8.

[93]Amel, Feroui, Messadi Mohammed and Bessaid Abdelhafid, Improvement of the hard exudates detection method used for computer- aided diagnosis of diabetic retinopathy, I.J. Image, Graphics and Signal Processing 4.4 (2012): 19-27.