Automatic Tortuosity Classification Using Machine Learning Approach

Abstract:

Retinopathy of Prematurity (ROP) is a vital cause of vision loss in premature infants, but early detection of its symptoms enables timely treatment and prevents blindness. Tortuosity is the major indicator of ROP that can potentially be automatically quantified. In this paper, which focuses on automatic tortuosity quantification and classification in images from infants at risk of ROP, we present a series of experiments on preprocessing, feature extraction, image feature selection and classification using nearest neighbor classifier. Fisher linear Discriminant analysis is used as a feature selection algorithm. We observe that the best feature set is a combination of two features: tortuosity as estimated based on combination of curvature of improved chain code and number of inflections and tortuosity as measured by inflection count metric. Accuracy, sensitivity and specificity are used as performance measures for the classifier. The results are validated against the judgments of expert ophthalmologists. The overall accuracy, sensitivity and specificity achieved on the best feature set are 95%, 95.65% and 96.74% respectively.

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 3143-3147
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.3143
• Online since: December 2012
• Authors: Rashmi Turior, Pornthep Chutinantvarodom, Bunyarit Uyyanonvara
• Keywords: Fisher Analysis, KNN Classifier, Tortuosity

References

[1] J J Capowski, J A Kylstra, S F Freedman, A numeric index based on spatial frequency for the tortuosity of retinal vessels and its application to plus disease in retinopathy of prematurity, Retina, 1995; 15: 6, 490-499.

DOI: 10.1097/00006982-199515060-00006

[2] S F Freedman, J A Kylstra, J J Capowski, T D Realini, C Rich, D Hunt, Observer sensitivity to retinal vessel diameter and tortuosity in retinopathy of prematurity: A model system, Journal of Pediatr Ophthalmol. Strabismus 1996; 33, 248-254.

DOI: 10.3928/0191-3913-19960701-10

[3] W E Hart, M Goldbaum; B Cote, P Kube, M R Nelson, Measurement and classification of retinal vascular tortuosity, International Journal of Medical Informatics, 1999; 53: 2, 239-252.

DOI: 10.1016/s1386-5056(98)00163-4

[4] 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

[5] 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

[6] 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

[7] Johnson MJ, Dougherty G, Robust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-line,. Med Eng Phys 2007; 29: 677-690.

DOI: 10.1016/j.medengphy.2006.07.008

[8] Enrico Grisan, Marco Foracchia, Alfredo Ruggeri, A novel method for the automatic grading of retinal vessel tortuosity, IEEE Transactions on Medical Imaging, VOL. 27, NO. 3, MARCH (2008).

DOI: 10.1109/tmi.2007.904657

[9] E. Trucco et. al., Modelling the tortuosity of Retinal Vessels: Does Calibre play a role?, IEEE trans on Biomedical Engg vol. 57, pp.2239-2247, (2010).

DOI: 10.1109/tbme.2010.2050771

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

[15] A. Webb, Statistical Pattern Recognition, Oxford University Press, 1999: Chapter 8.

[16] M. Dash and H. Liu, Feature Selection for Classification, Intelligent Data Analysis 1, 1997: 131-156.