Image processing-based model for tortuosity measurement of retinal blood vessels
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Chetia S, Nirmala S. Image processing-based model for tortuosity measurement of retinal blood vessels. MAIO [Internet]. 2019 Jul. 20 [cited 2021 Dec. 9];2(3):43-67. Available from:

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blood vessel pixels; hypertensive retinopathy; retinal blood vessels; tortuosity


Purpose: The study of retinal blood vessel morphology is of great importance in retinal image analysis. The retinal blood vessels have a number of distinct features such as width, diameter, tortuosity, etc. In this paper, a method is proposed to measure the tortuosity of retinal blood vessels obtained from retinal fundus images. Tortuosity is a situation in which blood vessels become tortuous, that is, curved or non-smooth. It is one of the earliest changes that occur in blood vessels in some retinal diseases. Hence, its detection at an early stage can prevent the progression of retinal diseases such as diabetic retinopathy, hypertensive retinopathy, retinopathy of prematurity, etc. The present study focuses on the measurement of retinal blood vessel tortuosity for the analysis of hypertensive retinopathy. Hypertensive retinopathy is a condition in which the retinal vessels undergo changes and become tortuous due to long term high blood pressure. Early recognition of hypertensive retinopathy signs remains an important step in determining the target-organ damage and risk assessment of hypertensive patients. Hence, this paper attempts to estimate tortuosity using image-processing techniques that have been tested on artery and vein segments of retinal images.

Design: Image processing-based model designed to measure blood vessel tortuosity.

Methods: In this paper, a novel image processing-based model is proposed for tortuosity measurement. This parameter will be helpful for analyzing hypertensive retinopathy. To test the eff ectiveness of the system in determining tortuosity, the method is first applied on a set of synthetically generated blood vessels. Then, the method is repeated on blood vessel (both artery and vein) segments extracted from retinal images collected from publicly available databases and on images collected from a local eye hospital. The blood vessel segment images that are used in the method are binary images where blood vessels are represented by white pixels (foreground), while black pixels represent the background. Vessels are then classified into normal, moderately tortuous, and severely tortuous by following the analysis performed on the images in the Retinal Vessel Tortuosity Data Set (RET-TORT) obtained from BioIm Lab, Laboratory of Biomedical Imaging (Padova, Italy). This database consists of 30 artery segments and 30 vein segments, which were manually ordered on the basis of increasing tortuosity by Dr. S. Piermarocchi, a retinal specialist belonging to the Department of Ophthalmology of the University of Padova (Italy). The artery and vein segments with the fewest number of turns are given a low tortuosity ranking, while those with the greatest number of turns are given a high tortuosity ranking by the expert. Based on this concept, our proposed method defines retinal image segments as normal when they present the fewest number of twists/turns, moderately tortuous when they present more twists/turns than normal but fewer than severely tortuous vessels, and severely tortuous when they present a greater number of twists/turns than moderately tortuous vessels. On implementing our image processing-based method on binary blood vessel segment images that are represented by white pixels, it is found that the vessel pixel (white pixels) count increases with increasing vessel tortuosity. That is, for normal blood vessels, the white pixel count is less compared to moderately tortuous and severely tortuous vessels. It should be noted that the images obtained from the different databases and from the local hospital for this experiment are not hypertensive retinopathy images. Instead, they are mostly normal eye images and very few of them show tortuous blood vessels.

Results: The results of the synthetically generated vessel segment images from the baseline for the evaluation of retinal blood vessel tortuosity. The proposed method is then applied on the retinal vessel segments that are obtained from the DRIVE and HRF databases, respectively. Finally, to evaluate the capability of the proposed method in determining the tortuosity level of the blood vessels, the method is tested with a standard tortuous database, namely, the RET-TORT database. The results are then compared with the tortuosity level mentioned in the database. It was found that our method is able to classify blood vessel images as normal, moderately tortuous, and severely tortuous, with results closely matching the clinical ordering provided by the expert in the RET-TORT database. Subjective evaluation was also performed by research scholars and postgraduate students to cross-validate the results.

Conclusion: The close correlation between the tortuosity evaluation using the proposed method and the clinical ordering of retinal vessels as provided by the retinal specialist in the RET-TORT database shows that, although simple, this method can evaluate the tortuosity of vessel segments effectively.
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Wang X, Cao H, Zhang J. Analysis of retinal images associated with hypertension and diabetes. In: Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the 2006 Jan 17 (pp. 6407-6410). doi:10.1109/IEMBS.2005.1615964.

Talu S. Characterization of retinal vessel networks in human retinal imagery using quantitative descriptors. Human and Veterinary Medicine. 2013;5(2):52-57.

Bhargava M, Wong TY. Current concepts in hypertensive retinopathy. Retinal Physician; 2013. Available from:

Wong TY, McIntosh R. Hypertensive retinopathy signs as risk indicators of cardiovascular morbidity and mortality. Br Med Bull. 2005 Sep 7;73-74 (1):57-70. doi:10.1093/bmb/ldh050.

Dougherty G, Varro J. A quantitative index for the measurement of the tortuosity of blood vessels. Med Eng Phys. 2000;22(8):567-574.

Oh KT. Ophthalmologic manifestations of hypertension, 2016. Available from:

Patasius M, Marozas V, Lukosevicius A, Jegelevicius D. Model based investigation of retinal vessel tortuosity as a function of blood pressure: preliminary results. In: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 2007 Aug 22 (pp. 6459-6462). doi:10.1109/IEMBS.2007.4353838

Mondal RN, Martin MA, Rani M, et al. Prevalence and risk factors of hypertensive retinopathy in hypertensive patients. J Hypertens. 2017;6(2):1-5. doi:10.4172/2167-1095.1000241

Duncan BB, Wong TY, Tyroler HA, Davis CE, Fuchs FD. Hypertensive retinopathy and incident coronary heart disease in high risk men. Br J Ophthalmol. 2002;86(9):1002-1006.

Ong YT, Wong TY, Klein R, et al. Hypertensive retinopathy and risk of stroke. Hypertension. 2013;62(4):706-711. doi:10.1161/HYPERTENSIONAHA.113.01414.

Sharbaf MA, Pourreza HR, Banaee T. A novel curvature-based algorithm for automatic grading of retinal blood vessel tortuosity. IEEE IEEE J Biomed Health Inform. 2016;20(2):586-595. doi:10.1109/JBHI.2015.2396198.

BioImLab-Laboratory of Biomedical Imaging- Retinal Vessel Tortuosity Data Set. Available from:

Grisan E, Foracchia M, Ruggeri A. A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans Med Imaging. 2008;27(3):310-319. doi:10.1109/TMI.2007.904657.

Cavallari M, Stamile C, Umeton R, Calimeri F, Orzi F. Novel method for automated analysis of retinal images: results in subjects with hypertensive retinopathy and CADASIL. Biomed Res Int. 2015:752957. doi: 10.1155/2015/752957.

Turior R, Onkaew D, Uyyanonvara B, Chutinantvarodom P, Orzi F. Quantification and classification of retinal vessel tortuosity. ScienceAsia. 2013;39(3):265-277. doi:10.2306/scienceasia1513-1874.2013.39.265

El Abbadi NK, Al Saadi EH. Automatic retinal vessel tortuosity measurement. Journal of Computer Science. 2013;9(11):1456-1460. doi: 10.3844/jcssp.

DRIVE database. Available from:

High-Resolution Fundus (HRF) Image Database. Available from:

Shome SK, Vadali SRK. Enhancement of diabetic retinopathy imagery using contrast limited adaptive histogram equalization. International Journal of Computer Science and Information Technologies. 2011;2(6):2694-2699.

Umbaugh S. Computer vision and image processing. New Jersey: Prentice Hall; 1998.

Singh TR, Roy S, Singh OI, Sinam T, Singh KM. A New adaptive thresholding technique in binarization. International Journal of Computer Science Issues. 2011;8(6):271-277.

Chetia S, Nirmala SR. Retinal blood vessel tortuosity measurement for analysis of hypertensive retinopathy. In: Innovations in Electronics, Signal Processing and Communication (IESC), 2017 International Conference on 2017 Apr 6 (pp. 45-50). doi:10.1109/IESPC.2017.8071862

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