DOI : https://doi.org/10.18517/ijaseit.8.4.6509

Quantification of Global Tortuosity in Retinal Blood Vessels

Astri Handayani (1), Tesi Dwi Nafia (2), Tati Rajab Mengko (3)
(1) Bandung Institute of Technology
(2) Bandung Institute of Technology
(3) Bandung Institute of Technology
Fulltext View | Download
How to cite (IJASEIT) :
Handayani, Astri, et al. “Quantification of Global Tortuosity in Retinal Blood Vessels”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4, Aug. 2018, pp. 1358-65, doi:10.18517/ijaseit.8.4.6509.
Citation Format :
Tortuosity is a parameter that indicates the tendency of a blood vessel segment to contain multiple twists and turns. Chronic hemodynamic changes in the body due to diabetes and hypertension will manifest as increased retinal vascular tortuosity, rendering tortuosity as a suitable indicator for diabetic and hypertensive retinopathy. Retinal tortuosity may be evaluated locally on a single segment or globally in the complete vascular network. Global tortuosity quantification consists of automated segmentation and partition of retinal vessel network, local tortuosity measurement, and global tortuosity index derivation from weighted combination of local tortuosity values. This paper proposes several weighting schemes and evaluates their performance when combined with different local tortuosity indexes. We perform rank correlation analysis to find the global tortuosity quantification that is most consistent with the ophthalmologists. Our results show that local tortuosity indexes that are robust to variations in scale and number of sampling points provide the best performance. Furthermore, weighting scheme based on chord length yields better results than the one based on arc length. The combination of Tortuosity Density (TD) local index and Tortuosity Density Global (TDG) weighting scheme provides the highest consistency with ophthalmologists, with the average rank correlation coefficient of 0.98 (p-value < 0.03).

M. B. Sasongko, T.Y. Wong, T.T. Nguyen, C.Y. Cheung, J.E. Shaw, R. Kawasaki, E.L. Lamoureux, and J.J. Wang. “Retinal vessel tortuosity and its relation to traditional and novel vascular risk markers in persons with diabetes”. Curr Eye Res, vol.41(4), pp.551-557, 2016.

M. Cavallari, C. Stamile, R. Umeton, F. Calimeri, and F. Orzi. “Novel method for automated analysis of retinal images: results in subjects with hypertensive retinopathy and CADASIL”. Biom Res Intl, 2015.

F. Oloumi, R. M. Rangayyan, and A. L. Ells. “Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity”. J of Med Img, vol.3(4), pp. 044.505, 2016.

F. Oloumi, R. M. Rangayyan, P. Casti, and A. L. Ells. “Computer-aided diagnosis of plus disease via measurement of vessel thickness in retinal fundus images of preterm infants”. Comp in Biol and Med, vol.66, pp.316-329, 2015.

J.C. Zenteno, J. Crespí­, B. Buentello-Volante, J.A. Buil, F. Bassaganyas, J.L. Vela-Segarra, and M.T. Marieges. “Next generation sequencing uncovers a missense mutation in COL4A1 as the cause of familial retinal arteriolar tortuosity”. Graefe’s Arch. Clin. Exp. Ophthalmol, vol.252(11), pp. 1789-1794, 2015.

W. Lotmar, A. Freiburghaus, and D. Bracher, “Measurement of vessel tortuosity on fundus photographs,” Graefe’s Arch. Clin. Exp. Ophthalmol, vol. 211, pp. 49-57, 1979.

T. Mapayi, J. R. Tapamo, S. Viriri, and A. O. Adio, “Automatic retinal vessel detection and tortuosity measurement,” Image Anal Stereol, 35:117-135, 2016.

M. A. Sharbaf, H. R. Pourreza, T. Banaee. “A novel curvature based algorithm for automatic grading of retinal blood vessel tortuosity,” IEEE J of Biomed and Health Inform. 2015.

W. E. Hart, M. Goldbaum, P. Kube, and M.R. Nelson, “Automated measurement of retinal vascular tortuosity,” AMIA Annual Fall Symposium Proceedings, 1997.

W. E. Hart, M. Goldbaum, B. Cote, and P. Kube, “Measurement and classification of retinal vascular tortuosity,” Int. J. Med. Informatics, vol.53, no. 23, pp. 239-52, Feb. 1999.

E. Grisan, M. Foracchia, and A. Ruggeri, “A novel method for the automatic grading of retinal vessel tortuosity,” IEEE Trans. on Med Img, vol.27, no.3, pp.310-319, 2008.

M. Patasius, V. Marozas, D. Jegelevicius, and A. Lukosevicius, “Eval-uation of tortuosity of eye blood vessels using the integral of square of derivative of curvature,” IFMBE Proc. 3rd Eur. Med. Biol. Eng. Conf. (EMBEC05), vol. 11. 2005.

K. V. Chandrinos, M. Pilu, R. B. Fisher, and P. Trahanias, “Image processing techniques for the quantification of atherosclerotic changes,” DAI Research paper, 1998.

K. G. Goh, H. Wynne, M. L. Lee, and H. Wang, K. Cios, Ed., “Adris: an automatic diabetic retinal image screening system,” Med. Data Min. Knowledge Discovery, pp. 181-21, 2001.

E. Bullitt, G. Gerig, S. M. Pizer, W. Lin, and S.R. Aylward, ”Measuring tortuosity of the intracerebral vasculature from MRA image,” Med. Image Anal., vol. 9, pp. 1163-1171, Sep. 2003.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).