A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images

Malihe Miri, Zahra Amini, Hossein Rabbani, Raheleh Kafieh

DOI:

Abstract


Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar–venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches.

Keywords


Arteries and veins; computer-aided diagnosis; medical image processing; retinal fundus images; retinal vessel classification

Full Text:

PDF

References


Nguyen TT, Wang JJ, Wong TY. Retinal vascular changes in prediabetes and prehypertension: New findings and their research and clinical implications. Diabetes Care 2007;30:2708-15.

Viswanath K, McGavin DD. Diabetic retinopathy: Clinical findings and management. Community Eye Health 2003;16:21-4.

Moss SE, Klein R, Klein BE. The 14-year incidence of visual loss in a diabetic population. Ophthalmology 1998;105:998-1003.

Aiello LM. Perspectives on diabetic retinopathy. Am J Ophthalmol 2003;136:122-35.

Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 2012;35:556-64.

Mohamed Q, Gillies MC, Wong TY. Management of diabetiretinopathy: A systematic review. JAMA 2007;298:902-16.

Fraz MM, Rudnicka AR, Owen CG, Strachan DP, Barman SA. Automated arteriole and venule recognition in retinal images using ensemble classification. 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol 3, January 05, 2014. IEEE. p. 194-202.

Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, et al. Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 2002;19:105-12.

Niemeijer M, van Ginneken B, Staal J, Suttorp-Schulten MS, Abramoff MD. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 2005;24:584-92.

Walter T, Klein JC, Massin P, Erginay A. A contribution of image processing to the diagnosis of diabetic retinopathy -Detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 2002;21:1236-43.

Faust O, Acharya UR, Ng EY, Ng KH, Suri JS. Algorithms for the

automated detection of diabetic retinopathy using digital fundus images: A review. J Med Syst 2012;36:145-57.

Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, et al. Blood vessel segmentation methodologies in retinal images - A survey. Comput Methods Programs Biomed 2012;108:407-33.

Mendonca AM, Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 2006;25:1200-13.

Ikram MK, de Jong FJ, Vingerling JR, Witteman JC, Hofman A, Breteler MM, et al. Are retinal arteriolar or venular diameters associated with markers for cardiovascular disorders? The Rotterdam Study. Invest Ophthalmol Vis Sci 2004;45:2129-34.

Sun C, Wang JJ, Mackey DA, Wong TY. Retinal vascular caliber: Systemic, environmental, and genetic associations. SurvOphthalmol 2009;54:74-95.

Hatanaka Y, Nakagawa T, Hayashi Y, Aoyama A, Zhou X, Hara T, et al. Automated detection algorithm for arteriolar narrowing on fundus images. Proc. 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), paper, vol 291, August 2005.

Muramatsu C, Hatanaka Y, Iwase T, Hara T, Fujita H. Automated detection and classification of major retinal vessels for determination of diameter ratio of arteries and veins. SPIE Medical Imaging, March 4, 2010. International Society for Optics and Photonics. p. 76240J.

Kondermann C, Kondermann D, Yan M. Blood vessel classification into arteries and veins in retinal images. Medical Imaging, March 8, 2007. International Society for Optics and Photonics. p. 651247.

Martinez-Perez ME, Hughes AD, Stanton AV, Thom SA, Chapman N, Bharath AA, et al. Retinal vascular tree morphology: A semiautomatic quantification. IEEE Trans Biomed Eng 2002;49:912-7.

Rothaus K, Rhiem P, Jiang X. Separation of the retinal vascular graph in arteries and veins. International Workshop on Graph- Based Representations in Pattern Recognition, June 11, 2007. Springer Berlin Heidelberg. p. 251-62.

Rothaus K, Jiang X, Rhiem P. Separation of the retinal vascular graph in arteries and veins based upon structural knowledge. Image Vis Comput 2009;27:864-75.

Estrada R, Allingham MJ, Mettu PS, Cousins SW, Tomasi C, Farsiu S. Retinal artery-vein classification via topology estimation. IEEE Trans Med Imaging 2015;34:2518-34.

Estrada R, Tomasi C, Schmidler SC, Farsiu S. Tree topology estimation. IEEE Trans Pattern Anal Mach Intell 2015;37: 1688-701.

Qureshi TA, Habib M, Hunter A, Al-Diri B. A manually-labeled, artery/vein classified benchmark for the DRIVE dataset. Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, June 20, 2013. IEEE. p. 485-8.

INSPIRE-AVR: Iowa normative 2015 set for processing images of the retina-artery vein ratio. Available from: http://webeye.ophth. uiowa.edu/component/k2/item/270. [Last accessed 2012].

Grisan E, Ruggeri A. A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol 1, September 17, 2003. IEEE. p. 890-3.

Ruggeri A, Grisan E, De Luca M. An automatic system for the estimation of generalized arteriolar narrowing in retinal images. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 22, 2007. IEEE. p. 6463-6.

Tramontan L, Grisan E, Ruggeri A. An improved system for the automatic estimation of the Arteriolar-to-Venular diameter Ratio (AVR) in retinal images. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 20, 2008. IEEE. p. 3550-3.

Color photography vs fluorescein angiography in the detection of diabetic retinopathy in the diabetes control and complications trial. The Diabetes Control and Complications Trial Research Group. Arch Ophthalmol 1987;105:1344-51.

Li H, Hsu W, Lee ML, Wang H. A piecewise Gaussian model for profiling and differentiating retinal vessels. Proceedings of the 2003 International Conference on Image Processing (ICIP), vol 1, September 14, 2003. IEEE. p. 1-1069.

Jelinek HF, Depardieu C, Lucas C, Cornforth DJ, Huang W, Cree MJ. Towards vessel characterization in the vicinity of the optic disc in digital retinal images. Image Vis Comput Conf, November 28, 2005. p. 2-7.

Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann Publishers; 2005. 560. ISBN 0-12-088407-0.

Efron B. Estimating the error rate of a prediction rule: Improvement on cross-validation. J Am Stat Assoc 1983;78:316-31.

Hall MA. Correlation-based feature selection for machine learning. Doctoral dissertation. The University of Waikato.

Narasimha-Iyer H, Beach JM, Khoobehi B, Roysam B. Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features. IEEE Trans Biomed Eng 2007;54:1427-35.

Niemeijer M, Staal J, Ginneken B, Loog M, Abramoff MD. DRIVE: Digital retinal images for vessel extraction; 2017. Available from: http://www.isi.uu.nl/Research/Databases/DRIVE.

Muramatsu C, Hatanaka Y, Iwase T, Hara T, Fujita H. Automated

selection of major arteries and veins for measurement of arteriolarto-

venular diameter ratio on retinal fundus images. Comput Med Imaging Graph 2011;35:472-80.

Vazquez SG, Cancela B, Barreira N, Penedo MG, Saez M. On the automatic computation of the arterio-venous ratio in retinal images: Using minimal paths for the artery/vein classification. 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), December 1, 2010. IEEE. p. 599-604.

Saez M, Gonzalez-Vazquez S, Gonzalez-Penedo M, Barcelo MA,

Pena-Seijo M, Coll de Tuero G, et al. Development of an automated system to classify retinal vessels into arteries and veins. Comput Methods Programs Biomed 2012;108:367-76.

Vazquez SG, Cancela B, Barreira N, Penedo MG, Rodrnguez- Blanco M, Seijo MP, et al. Improving retinal artery and vein classification by means of a minimal path approach. Mach Vis Appl 2013;24:919-30.

VICAVR-2: VARPA images for the computation of the arterio/ venular ratio, database; 2011. Available from: http://www.varpa.es/ vicavr2.html. [Last accessed 2011].

Niemeijer M, van Ginneken B, Abramoff MD. Automatic classification of retinal vessels into arteries and veins. SPIE Medical Imaging, February 26, 2009. International Society for Optics and Photonics. p. 72601F.

Niemeijer M, van Ginneken B, Abramoff MD. Automatic determination of the artery vein ratio in retinal images. SPIE Medical Imaging, March 4, 2010. International Society for Optics and Photonics. p. 76240I.

Niemeijer M, Xu X, Dumitrescu AV, Gupta P, van Ginneken B, Folk JC, et al. Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs. IEEE Trans Med Imaging 2011;30:1941-50.

Rothaus K, Jiang X. Classification of arteries and veins in retinal images using vessel profile features. 2011 International Symposium on Computational Models for Life Sciences (CMLS-11), vol 1371, no 1, 2011. AIP Publishing. p. 9-18.

Zamperini A, Giachetti A, Trucco E, Chin KS. Effective features for artery-vein classification in digital fundus images. 2012 25th International Symposium on Computer-Based Medical Systems (CBMS), June 20, 2012. IEEE. p. 1-6.

Relan D, MacGillivray T, Ballerini L, Trucco E. Retinal vessel classification: Sorting arteries and veins. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 3, 2013. IEEE. p. 7396-9.

Relan D, MacGillivray T, Ballerini L, Trucco E. Automatic retinal vessel classification using a least square-support vector machine in VAMPIRE. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 26, 2014. IEEE. p. 142-5.

Cheung CY, Hsu W, Lee ML, Wang JJ, Mitchell P, Lau QP, et al. A new method to measure peripheral retinal vascular caliber over an extended area. Microcirculation 2010;17:495-503.

Mirsharif Q, Tajeripour F, Pourreza H. Automated characterization

of blood vessels as arteries and veins in retinal images. Comput Med Imaging Graph 2013;37:607-17.

Joshi VS, Reinhardt JM, Garvin MK, Abramoff MD. Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks. PLoS One 2014;9:e88061.

Abramoff MD, Suttorp-Schulten MS. Web-based screening for diabetic retinopathy in a primary care population: The EyeCheck project. Telemed J e-Health 2005;11:668-74.

Dashtbozorg B, Mendonça AM, Campilho A. An automatic graphbased approach for artery/vein classification in retinal images. IEEE Trans Image Process 2014;23:1073-83.

VICAVR: VARPA images for the computation of the arterio/ venular ratio, database; 2009. Available from: http://www.varpa. es/vicavr.html. [Last accessed 2009].

EPIC-Norfolk. European Prospective Investigation of Cancer (EPIC); 2017. Available from: http://www.srl.cam.ac.uk/epic/.

Hatami N, Goldbaum M. Automatic Identification of Retinal Arteries and Veins in Fundus Images Using Local Binary Patterns; 2016. arXiv preprint arXiv:1605.00763.

STARE: Structured analysis of the retina; 2017. Available from: http://cecas.clemson.edu/ahoover/stare/.

Amini Z, Rabbani H. Classification of medical image modeling methods: A review. Curr Med Imaging Rev 2016;12: 130-48.


Refbacks

  • There are currently no refbacks.


 

  https://e-rasaneh.ir/Certificate/22728

https://e-rasaneh.ir/

ISSN : 2228-7477