Abstract
The investigation of artery vein changes over time is considered to be the significant diagnosis process of retinal diseases like diabetic retinopathy. The diagnosis includes the characteristics analysis of artery vein vessels, changes in its tortuosity level and artery vein ratio; hence, it is important to classify the artery and vein in a better way. Computer-aided diagnosis requires the automated classification of retinal artery and vein for diagnosing the progression of diseases. In this paper, a supervised classification with Bat algorithm is proposed to discriminate the artery and vein vessels in the retinal fundus images. A novel feature vector space, including both additive colour space as well as luminous chromaticity model colour space, is constructed. BAT algorithm is applied to select the feature group which improve the classification accuracy and also to reduce the dimensionality of feature space. The proposed method is developed and analyzed using the publicly available databases DRIVE, IOSTAR and STARE.
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Abbasi-Sureshjani S, Smit-Ockeloen I, Zhang J, Romeny BTH (2015) Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images, Image Analysis and Recognition. Springer, Berlin, pp 325–334
Abbasi Sureshjani S, Smit Ockeloen I, Bekkers E, Dashtbozorg B, ter Haar Romeny B (2016) Automatic Detection of Vascular Bifurcations and Crossings in Retinal Images Using Orientation Scores, In: The IEEE International Symposium on Biomedical Imaging (ISBI)
Bhuiyan A, Kawasaki R, Lamoureux E, Ramamohanarao K, Wong TY (2013) Retinal artery-vein caliber grading using color fundus imaging. Comput Methods Programs Biomed 111(1):104–114
Charbonnie J-P (2016) Automatic pulmonary artery-vein separation and classification in computed tomography using tree partitioning and peripheral vessel matching. IEEE Trans on Med Imaging 35(3):882–892
Council ES et al (2013) ESH/ESC Guidelines for the management of arterial hypertension. Eur Heart J 34:2159–2219
Dashtbozorg B, Mendonca AM, Campilho A (2014) An automatic graph-based approach for artery/vein classification in retinal images. IEEE Trans Image Process 23(3):1083–1093
de Moura J, Novo J, Rouco J et al (2019) J Digit Imaging 32:947. https://doi.org/10.1007/s10278-019-00235-x
Estrada R et al (2015) Retinal artery-vein classification via topology estimation. IEEE Trans Medical Imag 34(12):2518–2534
Franklin SW, Edward Rajan S (2014) Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features. Appl Soft Comput 22:94–100
Goyal S, Patterh MS (2016) Wireless Pers Commun 86:657. https://doi.org/10.1007/s11277-015-2950-9
Grisan R (2003) A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. Annual Int Conf IEEE Eng Med Biol Soc 2003:890–893
Haralick Robert M, Shanmugam K, Itshak D (1973) Textural features for image classification. IEEE Trans Syst, Man, and Cybern. SMC-3 6:610–621
Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imag 19:203–210
Huang F, Dashtbozorg B, Tan T, Romeny BTH (2018) Retinal artery/vein classification using genetic-search feature selection. Comput Methods Programs Biomed 161:197–207
Iida Y et al (2017) Morphological and functional retinal vessel changes in branch retinal vein occlusion: an optical coherence tomography angiography study’. Am J Ophthalmol 182:168–179
Jiang X, Mojon D (2003) Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Machine Intell 25:131–137
Kaur G, Rattan M, Jain C (2017) Optimization of Swastika slotted fractal antenna using genetic algorithm and bat algorithm for S-band utilities. Wireless Pers Commun 97:95–107. https://doi.org/10.1007/s11277-017-4495-6
Keith NM, Wagener HP, Barker NW (1939) Some different types of essential hypertension: their course and prognosis. Am J Med Sci 197:332–343
Kondermann C, Kondermann D, Yan M (2007) Blood vessel classification into arteries and veins in retinal images, Medical Imaging. International Society for Optics and Photonics, p. 651247
Kriplani H, Patel M, Roy S (2020) Prediction of arteriovenous nicking for hypertensive retinopathy using deep learning. In: Behera H, Nayak J, Naik B, Pelusi D (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore
Li H, Hsu W, Lee M, Wang H (2003) A piecewise Gaussian model for profiling and differentiating retinal vessels. Proc Int Conf Image Process 1:1069–1072
Mancia G et al (2013) ESH/ESC guidelines for the management of arterial hypertension: the task force for the management of arterial hypertension of the european society of hypertension (ESH) and of the european society of cardiology (ESC)’. Blood Press 2013(22):193–278
Mirsharif Q, Tajeripour F, Pourreza H (2013) Automated characterization of blood vessels as arteries and veins in retinal images’. Comput Med Imag Graph 37:607–617
Muramatsu C, Hatanaka Y, Iwase T, Hara T, Fujita H (2011) Automated selection of major arteries and veins for measurement of arteriolar venular diameter ratio on retinal fundus images. Comput Med Imag Graph 35:472–80
Narasimha Iyer H, Beach JM, Khoobehi B, Roysam B (2007) Automatic identification of retinal arteries and veins From dual-wavelength images using structural and functional features. IEEE Trans Biomed Eng 54(8):1427–1435
Niemeijer M, van Ginneken B, Abramoff MD (2009) Automatic classification of retinal vessels into arteries and veins. Proc SPIE Progr Biomed Opt Imag 2009:7260
Parr J, Spears G (1974) Mathematic relationships between the width of a retinal artery and the widths of its branches. Am J Ophthalmol 77:478–483
Perez J, Valdez F, Castillo O (2014) Bat algorithm comparison with genetic algorithm using benchmark functions. In: Castillo O, Melin P, Pedrycz W, Kacprzyk J (eds) Recent advances on hybrid approaches for designing intelligent systems. Studies in Computational Intelligence, vol 547. Springer, Cham
Preeti Kumar D (2017) Int J Inf Tecnol :411. https://doi.org/10.1007/s41870-017-0051-6
Rothaus K, Jiang X, Rhiem P (2009) Separation of the retinal vascular graph in arteries and veins based upon structural knowledge. Image Vis Comput 27(7):864–875
Sathananthavathi V, Indumathi G (2018) BAT algorithm inspired retinal vessel segmentation’. IT Image Process 12(11):2075–2083
Sazaka Ç, Nelson CJ, Obara B (2019) The Multiscale bowler-hat transform for blood vessel enhancement in retinal images. Pattern Recognit 88:739–750
Snell RS, Lemp MA (1998) Clinical Anatomy of the Eye. Wiley, NewYork, USA
Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Medical Imag 23:501–509
Sun C, Wang JJ, Mackey DA, Wong TY (2009) Retinal vascular caliber: systemic, environmental, and genetic associations. Survey Ophthalmol. 54(1):74–95
Tso MO, Jampol LM (1982) Pathophysiology of hypertensive retinopathy. Ophthalmology 89:1132–1145
Vazquez S, Cancela B, Barreira N, Penedo M, Saez M (2010) On the automatic computation of the arterio-venous ratio in retinal images: Using minimal paths for the artery/vein classification, Proc. Int. Conf. Digital Image Comput. Tech Appl 2010:599–604
Wang J, Zhou Q (2020) Correlation between coronary heart disease and the retinal arteriovenous ratio. In: Wang N (ed) Integrative Ophthalmology. Advances in Visual Science and Eye Diseases, vol 3. Springer, Singapore
Wong TY, Mitchell P (2004) Hypertensive retinopathy. New Engl J Med 351:2310–2317
Xiayu X, Ding WX, Abramoff MD, Cao R (2017) Comput Methods Programs Biomed 141:3–9
Yancang L, Zhen Y (2019) KSCE J Civ Eng 23:2636. https://doi.org/10.1007/s12205-019-2119-2
Yang XS (2010) A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NISCO). Studies in Computational Intelligence, vol 284. Springer, Berlin, pp 65–74
Yin X, Irshad S, Zhang Y (2019) Artery/vein classification of retinal vessels using classifiers fusion. Health Inf Sci Syst 7:26. https://doi.org/10.1007/s13755-019-0090-4
Zhang J et al (2017) Retinal vessel delineation using a brain-inspired wavelet transform and random forest. Pattern Recognit 69:107–123
Acknowledgements
Authors would like to acknowledge Principal and Management of Mepco Schlenk Engineering College, Sivakasi for their encouragement and support for this research work. Authors would also like to thank the anonymous reviewers for their valuable comments and suggestions.
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Sathananthavathi, V., Indumathi, G. BAT optimization based Retinal artery vein classification. Soft Comput 25, 2821–2835 (2021). https://doi.org/10.1007/s00500-020-05339-z
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DOI: https://doi.org/10.1007/s00500-020-05339-z