Automatic retinal vessel tortuosity measurement using curvature of improved chain code

Onkaew, Danu; Turior, Rashmi; Uyyanonvara, Bunyarit; Akinori, Nishihara; Sinthanayothin, Chanjira

doi:10.1109/inecce.2011.5953872

Cited by 35 publications

(33 citation statements)

References 8 publications

(13 reference statements)

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“…Retinal vessel network tortuosity is the measure of twists and curvature of a vessel Onkaew et al;Chakravarty and Sivaswamy;. Several clinical studies have described the relation between vessel tortuosity and retinopathies such as DR, ROP (Scheie;1953;Capowski et al;.…”

Section: Vessel Tortousity Measurementmentioning

confidence: 99%

“…1. One of the first changes in vessels morphology to occur in DR patients is the increase in vessel tortuosity (Scheie;1953;Kristinsson et al;Owen et al;Taarnhøj et al;Onkaew et al;Chakravarty and Sivaswamy;. Tortuosity has been shown to be a more reliable vascular parameter in differentiating ROP severity than vessel width (Capowski et al;.…”

Section: Introductionmentioning

confidence: 99%

“…Grisan et al (2003Grisan et al ( , 2008 implemented automatic tortuosity measures using arc to chord ratio and the points of changing curvature sign. The vessel extraction and inflection point placement was however done manually Onkaew et al;. Capowski et al (1995) addressed tortuosity measurement using the number of direction changes in vessel course.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Retinal Vessel Detection and Tortuosity Measurement

Mapayi¹,

Tapamo²,

Viriri³

et al. 2016

Image Anal Stereol

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As retinopathies continue to be major causes of visual loss and blindness worldwide, early detection and management of these diseases will help achieve significant reduction of blindness cases. However, an efficient automatic retinal vessel segmentation approach remains a challenge. Since efficient vessel network detection is a very important step needed in ophthalmology for reliable retinal vessel characterization, this paper presents the study on the combination of difference image and k-means clustering for the segmentation of retinal vessels. Stationary points in the vessel center-lines are used to model the detection of twists in the vessel segments. The combination of arc-chord ratio with stationary points is used to compute tortuosity index. Experimental results show that the proposed k-means combined with difference image achieved a robust segmentation of retinal vessels. A maximum average accuracy of 0.9556 and a maximum average sensitivity of 0.7581 were achieved on DRIVE database while a maximum average accuracy of 0.9509 and a maximum average sensitivity of 0.7666 were achieved on STARE database. When compared with the previously proposed techniques on DRIVE and STARE databases, the proposed technique yields higher mean sensitivity and mean accuracy rates in the same range of very good specificity. In a related development, a non-normalized tortuosity index that combined distance metric and the vessel twist frequency proposed in this paper also achieved a strong correlation of 0.80 with the expert ground truth.

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Section: Vessel Tortousity Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Retinal Vessel Detection and Tortuosity Measurement

Mapayi¹,

Tapamo²,

Viriri³

et al. 2016

Image Anal Stereol

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“…Robust metrics employing unit speed parametrization for quantifying vascular tortuosity in terms of 3-D curvature is also defined 19 . In previous work, we have defined and evaluated methods for automatic measurement of retinal vessel tortuosity in infant retinal images using PCA 20 and combination of inflection count and curvature of improved chain code 21 , and curvature based on chain code rules 22 . In experiments on comparable data sets, the accuracy for these methods was 100% 21,22 .…”

Section: Introductionmentioning

confidence: 99%

“…In previous work, we have defined and evaluated methods for automatic measurement of retinal vessel tortuosity in infant retinal images using PCA 20 and combination of inflection count and curvature of improved chain code 21 , and curvature based on chain code rules 22 . In experiments on comparable data sets, the accuracy for these methods was 100% 21,22 . While these results are encouraging, they are limited by suboptimal feature selection and unsupervised classification techniques.…”

Section: Introductionmentioning

confidence: 99%

Quantification and classification of retinal vessel tortuosity

Turior¹,

Onkaew²,

Uyyanonvara³

et al. 2013

ScienceAsia

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ABSTRACT:The clinical recognition of abnormal retinal tortuosity enables the diagnosis of many diseases. Tortuosity is often interpreted as points of high curvature of the blood vessel along certain segments. Quantitative measures proposed so far depend on or are functions of the curvature of the vessel axis. In this paper, we propose a parallel algorithm to quantify retinal vessel tortuosity using a robust metric based on the curvature calculated from an improved chain code algorithm. We suggest that the tortuosity evaluation depends not only on the accuracy of curvature determination, but primarily on the precise determination of the region of support. The region of support, and hence the corresponding scale, was optimally selected from a quantitative experiment where it was varied from a vessel contour of two to ten pixels, before computing the curvature for each proposed metric. Scale factor optimization was based on the classification accuracy of the classifiers used, which was calculated by comparing the estimated results with ground truths from expert ophthalmologists for the integrated proposed index. We demonstrate the authenticity of the proposed metric as an indicator of changes in morphology using both simulated curves and actual vessels. The performance of each classifier is evaluated based on sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and positive likelihood ratio. Our method is effective at evaluating the range of clinically relevant patterns of abnormality such as those in retinopathy of prematurity. While all the proposed metrics are sensitive to curved or kinked vessels, the integrated proposed index achieves the best sensitivity and classification rate of 97.8% and 93.6%, respectively, on 45 infant retinal images.

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