Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review
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Abstract
Diabetic detinopathy (DR) is effect of diabetes mellitus to the human vision that is the major cause of blindness. Early diagnosis of DR is an important requirement in diabetes treatment. Retinal fundus image is commonly used to observe the diabetic retinopathy symptoms. It can present retinal features such as blood vessel and also capture the pathologies which may lead to DR. Blood vessel is one of retinal features which can show the retina pathologies. It can be extracted from retinal image by image processing with following stages: pre-processing, segmentation, and post-processing. This paper contains a review of public retinal image dataset and several methods from various conducted researches. All discussed methods are applicable to each researcher cases. There is no further analysis to conclude the best method which can be used for general cases. However, we suggest morphological and multiscale method that gives the best accuracy in segmentation.
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Aras, R. A., Lestari, T., Nugroho, H. A., & Ardiyanto, I. (2016). Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review. Communications in Science and Technology, 1(1). https://doi.org/10.21924/cst.1.1.2016.13
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This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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2. S. Wild, G. Roglic, A. Green, R. Sicree, H. King, Global prevalence of diabetes estimates for the year 2000 and projection for 2030, World Health. 27 (2004) 1047–1053.
3. WHO, Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia, in: Switzerland, 2006: p. 50.
4. American Optometric Association, Diabetes eye care of the patient with diabetes mellitus, 2014.
5. S. You, E. Bas, D. Erdogmus, Principal curve based retinal vessel segmentation towards diagnosis of retinal diseases, IEEE Int. Conf. Healthc. Informatics Imaging Syst. Biol. 11 (2011) 331–337.
6. M.E. Martinez-Perez, A.D. Hughes, A. V. Stanton, S. a. Thom, N. Chapman, A. a. Bharath, K.H. Parker, Retinal vascular tree morphology: A semi-automatic quantification, IEEE Trans. Biomed. Eng. 49 (2002) 912–917.
7. C.G. Owen, A.R. Rudnicka, C.M. Nightingale, R. Mullen, S. a. Barman, N. Sattar, D.G. Cook, P.H. Whincup, Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children; The child heart and health study in England (CHASE), Arterioscler. Thromb. Vasc. Biol. 31 (2011) 1933–1938..
8. M. Preethi, R. Vanithamani, Review of retinal blood vessel detection methods for automated diagnosis of Diabetic Retinopathy, Adv. Eng. Sci. (2012) 262–265.
9. C.G. Owen, S.A. Barman, Blood vessel segmentation methodologies in retinal images – A survey, Comput. Methods Programs Biomed. 108 (2012) 407–433..
10. N. Singh, L. Kaur, A survey on blood vessel segmentation methods in retinal images, 2015 Int. Conf. Electron. Des. Comput. Networks Autom. Verif. (2015) 23–28.
11. S. Chhabra, B. Bhushan, Supervised pixel classification into arteries and veins of retinal images, Proc. Int. Conf. Innov. Appl. Comput. Intell. Power, Energy Control. with Their Impact Humanit. CIPECH 2014. (2014) 59–62.
12. Y. Qian Zhao, X. Hong Wang, X. Fang Wang, F.Y. Shih, Retinal vessels segmentation based on level set and region growing, Pattern Recognit. 47 (2014) 2437–2446.
13. E.T.D. Ecencière, X.I.Z. Hang, G.U.Y.C. Azuguel, B.R.L. Aÿ, B. Éatrice, C. Ochener, C.A.T. Rone, P.H.G. Ain, J.O.H.N. Ichard, O.R. Arela, P.A.M. Assin, A.L.I.E. Rginay, B.É.C. Harton, J.E.A.N.L.K. Lein, Feedback on a publicly distributed image database: the messidor database, (2014) 231–234.
14. U.T.V. Nguyen, A. Bhuiyan, L. a. F. Park, K. Ramamohanarao, An effective retinal blood vessel segmentation method using multi-scale line detection, Pattern Recognit. 46 (2013) 703–715.
15. D. Relan, T. Macgillivray, L. Ballerini, E. Trucco, Automatic retinal vessel classification using a least square- support vector machine in VAMPIRE *, (2014) 142–145.
16. M.M. Fraz, P. Remagnino, A. Hoppe, S. a. Barman, A. Rudnicka, C. Owen, P. Whincup, A model based approach for vessel caliber measurement in retinal images, 8th Int. Conf. Signal Image Technol. Internet Based Syst. SITIS 2012r. (2012) 129–136.
17. A. Lisowska, R. Annunziata, G.K. Loh, D. Karl, E. Trucco, An experimental assessment of five indices of retinal vessel tortuosity with the RET-TORT public dataset, (2014) 5414–5417.
18. M.D. Saleh, C. Eswaran, A. Mueen, An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection, J. Digit. Imaging. 24 (2011) 564–572.
19. M.B. Patwari, Classification and calculation of retinal blood vessels parameters, (n.d.) 1–7.
20. a. Elbalaoui, M. Fakir, K. Taifi, A. Merbouha, Automatic detection of blood vessel in retinal images, 2016 13th Int. Conf. Comput. Graph. Imaging Vis. (2016) 324–332.
21. A. Kumar, A.K. Gaur, M. Srivastava, A segment based technique for detecting exudate from retinal fundus image, Procedia Technol. 6 (2012) 1–9.
22. M.M. Fraz, S.A. Barman, P. Remagnino, An approach to localize the retinal blood vessels using bit planes and centerline detection, Comput. Methods Programs Biomed. 108 (2012) 600–616.
23. L. Xu, S. Luo, A novel method for blood vessel detection from retinal images, Biomed. Eng. Online. 9 (2010) 14.
24. M. Foracchia, E. Grisan, A. Ruggeri, Luminosity and contrast normalization in retinal images, Med. Image Anal. 9 (2005) 14.
25. M. Vlachos, E. Dermatas, Multi-scale retinal vessel segmentation using line tracking, Comput. Med. Imaging Graph. 34 (2010) 213–227.
26. K.A. Goatman, A.D. Fleming, S. Philip, G.J. Williams, J.A. Olson, P.F. Sharp, Detection of new vessels on the optic disc using retinal photographs, Med. Imaging, IEEE Trans. 30 (2011) 972–979.
27. Q. Li, J. You, D. Zhang, Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses, Expert Syst. Appl. 39 (2012) 7600–7610.
28. W. Azani, M.- Ieee, H. Yazid, S. Bin Yaacob, A Review : comparison between different type of filtering methods on the contrast variation retinal images, (2014) 28–30.
29. M.U. Akram, I. Jamal, A. Tariq, J. Imtiaz, Automated segmentation of blood vessels for detection of proliferative diabetic retinopathy, Biomed. Heal. Informatics. (2012) 232–235.
30. M.D. Saleh, C. Eswaran, An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection, Comput. Methods Programs Biomed. 108 (2012) 186–196.
31. K. Sun, Z. Chen, S. Jiang, Y. Wang, Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram, J. Med. Syst. 35 (2011) 811–824.
32. L. Giancardo, F. Meriaudeau, T.P. Karnowski, Y. Li, K.W. Tobin, E. Chaum, Automatic retina exudates segmentation without a manually labelled training set, Biomed. Imaging From Nano to Macro, 2011 IEEE Int. Symp. On. IEEE. (2011) 1396–1400.
33. M.U. Akram, A. Tariq, S. Nasir, S.A. Khan, Gabor Wavelet Based Vessel Segmentation in Retinal Images, IEEE. (2009).
34. M. Tamilarasi, K. Duraiswamy, Genetic based fuzzy seeded region growing segmentation for diabetic retinopathy images, Comput. Commun. Informatics Int. Conf. On. IEEE. (2013) 1–5.
35. B. Antal, A. Hajdu, An ensemble-based system for microaneurysm detection and diabetic retinopathy grading, Biomed. Eng. IEEE Trans. 6 (2012) 1720–1726.
36. R. Bhattacharhjee, M. Chakraborty, Exudates, retinal and statistical features detection from diabetic retinopathy and normal fundus images: an automated comparative approach, Natl. Conf. Comput. Commun. Syst. Organ. by IEEE. (2012) 978.
37. S. You, E. Bas, D. Erdogmus, J. Kalpathy-Cramer, Principal curved based retinal vessel segmentation towards diagnosis of retinal diseases, Proc. - 2011 1st IEEE Int. Conf. Healthc. Informatics, Imaging Syst. Biol. HISB 2011. (2011) 331–337.
38. G.S. Ramlugun, V.K. Nagarajan, C. Chakraborty, Small retinal vessel extraction towards proliferative diabetic retinopathy screening, Expert Syst. with Appl. 39 (2012) 1141–1146.
39. B. Antal, I. Lazar, A. Hajdu, Z. Torok, A. Csutak, T. Peto, A Multi-level ensemble-based system for detecting microaneurysms in fundus images, 4th Int. Work. Soft Comput. Appl. IEEE. 10 (2010) 137–142.
40. A.. Aibinu, M.. Iqbal, A.. Shafie, M.J.. Salami, M. Nilsson, Vascular intersection detection in retina fundus images using a new hybrid approach, Comput. Biol. Med. 40 (2010) 91–89.
41. K. Ram, Y. Babu, J. Sivanwamy, Curvature orientation histogram for detection and matching of vascular landmarks in retinal images, (2009).
42. S. Yacin, Retinopathy based on brightness variations in SDOCT retinal images, (2015).
43. E. Moghimirad, S. Hamid Rezatofighi, H. Soltanian-Zadeh, Retinal vessel segmentation using a multi-scale medialness function, Comput. Biol. Med. 42 (2012) 50–60.
44. A.. Frangi, W.. Niessen, K.. Vincken, M.. Viergever, W. William, C. Alan, D. Scott, Multiscale vessel enhancement filtering, Med. Image Comput. Comput. Interv. MICCAI 98. (1998) 130.
45. M.E. Martinez-perez, A.D. Hughes, A.V. Stanton, S.A. Thom, A.A. Bharath, K.H. Parker, Retinal blood vessel segmentation by means of scale-space analysis and region growing, Proc. Second Int. Conf. Med. Image Comput. Comput. Interv. Springer-Verlag, London, UK,. (1999).
46. M. Vlacos, E. Dermatas, Multi-scale retinal vessel segmentation using line tracking, Comput. Med. Imaging Graph. (n.d.) 213–227.
47. Y.C. Tsai, H.J. Lee, M.Y.C. Chen, Adaptive segmentation of vessels from coronary angiograms using multi-scale filtering, Proc. - 2013 Int. Conf. Signal-Image Technol. Internet-Based Syst. SITIS 2013. (2013) 143–147.
48. Z. Han, Y. Yin, X. Meng, G. Yang, X. Yan, Blood vessel segmentation in pathological retinal image, (2014) 960–967.
49. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum, Detection of blood vessels in retinal images using two dimensional matched filters, IEEE Trans. Med. Imaging. 8 (1989) 263–269.
50. A.D. Hoover, V. Koznetsova, M. Goldbaum, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Trans. Med. Imaging. 19 (2000) 203–210.
51. M. Al-Rawi, M. Qutaishat, M. Arrar, An improved matched filter for blood vessel detection of digital retinal images, Comput. Biol. Med. 37 (2007) 262–267.
52. M.. Cinsdikici, D. Aydin, Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm, Comput. Methods Programs Biomed. 96 (2009) 85–95.
53. J. Odstrcilik, R. Kolar, T. Kubena, P. Cernosek, A. Budai, J. Hornegger, J. Gazarek, O. Svoboda, J. Jan, E. Angelopoulou, Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database, IET Image Process. 7 (2013) 373–383.
54. S. Fazli, a I. Transformations, A novel retinal vessel segmentation based on local adaptive histogram equalization, (2013) 131–135.
55. Y. Xiang, X. Gao, B. Zou, C. Zhu, C. Qiu, X. Li, Segmentation of retinal blood vessels based on divergence and bot-hat Transform, (2014) 4–8.
56. R.B. Kawadiwale, Evaluation of algorithms for segmentation of retinal blood vessels, 00 (2015).
57. F. Farokhian, H. Demirel, Blood vessels detection and segmentation in retina using gabor filters, (2013) 104–108.
58. R. Masooomi, A. Mohtadizadeh, Retinal vessel segmentation using non-subsampled contourlet transform and multi-scale line detection, (2014).
59. F. Zana, J.C. Klein, A multimodal registration algorithm of eye fundus images using vessels detection and hough transform, IEEE Trans. Med. Imaging 18. (1999) 419–428.
60. K. Sun, Z. Chen, S. Jiang, Y. Wang, Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram, J. Med. Syst. (2010).
61. M.M. Fraz, S.A. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, A. Rudnicka, C.G. Owen, An approach to localize the retinal blood vessels using bit planes and centerline detection, Comput. Methods Programs Biomed. (2011).
62. D. Singh, A new morphology based approach for blood vessel segmentation in retinal images, (2014).
63. B. Sindhu, J. Jeeva, Automated retinal vessel segmentation using morphological operation and threshold, Ijser.Org. 4 (2013) 1614–1617.
64. S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, G. Yang, Hierarchical retinal blood vessel segmentation based on feature and ensemble learning, Neurocomputing. 149 (2015) 708–717.
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