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Vessel Tortuosity Index (VTI)

version 1.0.2 (9.67 KB) by Maz M. Khansari
This is a tool for reliable computation of vessel tortuosity in 2D and theoretical work for assessment of curvature of curvilinear shapes.

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Updated 11 Oct 2019

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This entry contains files for calculating tortuosity of a vessel or any curvilinear shape in 2D based on the centerline of the object. We developed this tool originally for quantifying tortousity of retinal vessels but it can be generalized to other objects. Please see the following paper for additional description and mathematical derivation. Also, if you are using this tool, please cite the paper.

Vessel tortuosity index (VTI) is determined based on multiple tortousity and curvature parameters of the centerline and was shown to perform better than available method in matching with visual perception of tortousity.

Run demo_VTI.m for a quick demo of the parameter used for calculating VTI and sample result. Note the main result is VTI but in the demo intermediate parameters are also exported to help with understanding of the concept.

The main function is 'vessel_tortousity_index(x,y,is_show)' and the user suppose to provide x and y coordinates of a centerline. is_show takes values of 1 or 0. Value of 1 provides visualization of the centerline and the parameters extracted for tortuosity quantification.

Other functions will be included to help with extracting and smoothing vessel centerline. Note that in real-world applications, smoothing is crucial for meaningful tortuosity analysis.

Khansari, et al. "Method for quantitative assessment of retinal vessel tortuosity in optical coherence tomography angiography applied to sickle cell retinopathy." Biomedical optics express 8.8 (2017):3796-3806.

Cite As

Maz M. Khansari (2020). Vessel Tortuosity Index (VTI) (https://www.mathworks.com/matlabcentral/fileexchange/72986-vessel-tortuosity-index-vti), MATLAB Central File Exchange. Retrieved .

Khansari, Maziyar M., et al. “Automated Fine Structure Image Analysis Method for Discrimination of Diabetic Retinopathy Stage Using Conjunctival Microvasculature Images.” Biomedical Optics Express, vol. 7, no. 7, The Optical Society, June 2016, p. 2597, doi:10.1364/boe.7.002597.

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Comments and Ratings (3)

Drew Cardwell

Sherry Khansari

Maz Khansari

MATLAB Release Compatibility
Created with R2017b
Compatible with R2013a to R2019a
Platform Compatibility
Windows macOS Linux
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