Semi-Automated Computer Analysis of Retinal Vessels in Premature Infants

Abstract:

Almost all ocular and systemic diseases affect blood vessel attributes (tortuosity, length, width, and curvature). Quantitative measurements of these attributes could thus provide useful tool for diagnosing the severity of several diseases. However, it is still unclear how best to represent the attribute values of multiple vessels in a single image. Graphical user interface (GUI) is a promising step towards the development of a semi-automated computer assisted tool. The objective of this study is to develop a GUI for effective observation and robust retinal blood vessels analysis by ophthalmologists and to comprehend the distribution of vessels attributes. Blood vessels from 45 digital fundus images of infant retina are extracted, its centerline is delineated and tortuosity is analyzed from different putative and proposed techniques to provide reliable and comprehensive information for the retinal vasculature. K means clustering technique is used for classification analysis of different tortuosity metrics and its performance is evaluated based on sensitivity, specificity, and accuracy. The results are validated by comparing with expert ophthalmologists’ ground truths. Among the various proposed tortuosity metrics, one of our tortuosity indexes attains the highest classification accuracy of 91.42% with sensitivity and specificity of 86.36% and 97.82% respectively.

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 2962-2968
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.2962
• Online since: December 2012
• Authors: Rashmi Turior, Pornthep Chutinantvaron, Bunyarit Uyyanonvara
• Keywords: Graphical User Interface (GUI), K-Means Clustering, Retinal Vessels

References

[1] Wallace DK, Zhao Z, Freedman SF, A pilot study using ROPtool, to quantify plus disease in retinopathy of prematurity. J AAPOS2007; 11(4): 381-387.

DOI: 10.1016/j.jaapos.2007.04.008

[2] Henegan C, Flynn J, O'Keefe M, et al. Characterization of changesin blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Med Image Anal. 2002; 6(4): 407– 429.

DOI: 10.1016/s1361-8415(02)00058-0

[3] Swanson C, Cocker KD, Parker KH, et al. Semiautomated computer analysis of vessel growth in preterm infants without and with ROP. Br J Ophthalmol. 2003; 87(12): 1474–1477.

DOI: 10.1136/bjo.87.12.1474

[4] Gelman R, Martinez-Perez ME, Vanderveen DK, et al. Diagnosis of plus disease in retinopathy of prematurity using Retinal Image multiScale Analysis. Invest Ophthalmol Vis Sci. 2005; 46(12): 4734-4738.

DOI: 10.1167/iovs.05-0646

[5] Lassada Sukkaew, Bunyarit Uyyanonvara, Sarah Barman, Alistair Fielder, Ken Cocker, Automatic extraction of the structure of the retinal blood vessel network of premature infants, Journal of Medical Association of Thailand , Vol. 90, No. 9, 2007, pp.1780-1792.

[6] Lassada Sukkaew, Bunyarit Uyyanonvara, Stanislav S. Makhanov, Sarah Barman, and Pannet Pangputhipong, Automatic Tortuosity- Based Retinopathy of Prematurity screening system, IEICE Trans. INF. & SYST., VOL. E91-D, NO. 12 DECEMBER (2008).

DOI: 10.1093/ietisy/e91-d.12.2868

[7] Venkatalakshmi, B., Saravanan, V. and Niveditha, G.J., Graphical User Interface for enhanced retinal image analysis for diagnosing diabetic retinopathy, Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference, pp.610-613, (2011).

DOI: 10.1109/iccsn.2011.6014967

[8] Kee Yong Pang, Lila Iznita I, Ahmad Fadzil M H, Hanung A. N., Hermawan N. and Vijanth S A, Segmentation of Retinal Vasculature in Colour Fundus Images, Innovative Technologies in Intelligent Systems and Industrial Application 2009 (CITISIA 2009), pp.398-401, (2009).

DOI: 10.1109/citisia.2009.5224176

[9] Delori et al (2001): "Macular pigment density measured by autofluorescence spectrometry: comparison with reflectometry and heterochromatic flicker photometry. J. Opt. Soc. Am. 18: 121230.

DOI: 10.1364/josaa.18.001212

[10] Danu Onkaew, Rashmi Turior, Bunyarit Uyyanonvara, Nishihara Akinori, Chanjira Sinthanayothin, Automatic Retinal Vessel Tortuosity Measurement using Curvature of Improved Chain Code, International Conference on Electrical, Control and Computer Engineering (InECCE 2011), pp.183-186.

DOI: 10.1109/inecce.2011.5953872

[11] Rashmi Turior, Danu Onkaew, Toshiaki Kondo, Bunyarit Uyyanonvara, A novel approach for quantification of retinal vessel tortuosity based on principal component analysis, Proceeding of 8th Electrical Engineering/Electronics, Computer, Telecommunication & Information Technology Association Conference 2011 (ECTICON 2011), pp.1023-1026.

DOI: 10.1109/ecticon.2011.5948017

[12] RashmiTurior , Danu Onkaew, Bunyarit Uyyanonvara, Robust Metrics for Retinal Vessel Tortuosity Measurement using Curvature Based on Improved Chain Code , International Conference on Biomedical Engineering (ICBME 2011), pp.217-221, 10-12 December 2011 Manipal, India (December (2011).

DOI: 10.1109/inecce.2011.5953872

[13] Aslam T, Fleck B, Patton N, Trucco M, Azegrouz H (2009) Digital image analysis of plus disease in retinopathy of prematurity. Acta Ophthalmol 87: 368-377.

DOI: 10.1111/j.1755-3768.2008.01448.x

[14] W. Lotmar,A. Freiburghaus, and D. Bracher. Measurement of vessel tortuosity on fundus photographs, " Graefe, s Archive for clinical and experimental Ophthalmology, vol. 211, pp.49-57, (1979).

DOI: 10.1007/bf00414653

[15] E. Bullit, G. Gerig, S. Pizer, W. Lin, and S. Aylward, Measuring tortuosity of the intracerebral vasculature from MRA images, IEEE Trans. Med. Imaging, vol. 22, no. 9, pp.1163-1171, (2003).

DOI: 10.1109/tmi.2003.816964

[16] Smedby O, Hogman N, Nilsson S, Erikson U, Olsson AG, Walldius G. Two-dimentional tortuosity of the superficial femoral artery in early atherosclerosis,. J Vasc Res 1993; 30: 181-91.

DOI: 10.1159/000158993

[17] Geoffrey Dougherty and J. Varro, A quantitative index for the measurement of the tortuosity of blood vessels, Medical Engineering & Physics, vol. 22, pp.567-574, (2000).

DOI: 10.1016/s1350-4533(00)00074-6

[18] M Ramaswamy et al, A Study and Comparison of Automated Techniques for Exudate Detection Using Digital Fundus Images of Human Eye: A Review for Early Identification of Diabetic Retinopathy Int. J. Comp. Tech. Appl., Vol 2 (5), 1503-1516.

[19] Sanchez, C.I.; Hornero, R.; Lopez, M.I.; Poza, J. Retinal Image Analysis to Detect and Quantify Lesions Associated with Diabetic Retinopathy. In Internat. Conf. on Engineering in Medicine and Biology Society (EMBC), 2004, p.1624 – 1627.

DOI: 10.1109/iembs.2004.1403492