Abstract-Measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. Screening of Retinopathy of Prematurity (ROP), a disease of eye that affects premature infants, for example, depends crucially on automatic tortuosity evaluation. Quite a few techniques for tortuosity measurement and classification have been proposed, but they do not always match the clinical concept of tortuosity. In this paper, we propose the alternative method of automatic tortuosity measurement for retinal blood vessels that uses the curvature calculated from improved chain code algorithm taking the number of inflection point into account. The tortuosity calculated from the proposed method is independent of the segmentation of vessel tree. Our algorithm can automatically classify the image as tortuous or non-tortuous. The test results are verified against two expert ophthalmologists. For an optimal set of training parameters the prediction is as high as 100% on 18 images.

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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Tortuosity is one of the first manifestations of many retinal diseases such as those due to retinopathy of prematurity (ROP), hypertension, stroke, diabetes and cardiovascular diseases. An automatic evaluation and quantification of retinal vessel tortuosity would help in the early detection of such retinopathies and other systemic diseases. This paper proposes a new approach based on principal component analysis (peA), for the evaluation of tortuosity in vessels extracted from digital fundus images. One of the strength of the proposed algorithm is that the index is independent of translation, rotation and scaling.Measures are adopted such that the proposed approach matches with the clinical concept of tortuosity.The algorithm is compared with other available tortuosity measures. We have demonstrated its validity as an indicator of changes in morphology using simulated shapes. It is superior to other putative indices, presented previously in literature.

Rashmi TURIOR †a) , Student Member, Danu ONKAEW †b) , and Bunyarit UYYANONVARA †c) , Nonmembers SUMMARYAutomatic vessel tortuosity measures are crucial for many applications related to retinal diseases such as those due to retinopathy of prematurity (ROP), hypertension, stroke, diabetes and cardiovascular diseases. An automatic evaluation and quantification of retinal vascular tortuosity would help in the early detection of such retinopathies and other systemic diseases. In this paper, we propose a novel tortuosity index based on principal component analysis. The index is compared with three existant indices using simulated curves and real retinal images to demonstrate that it is a valid indicator of tortuosity. The proposed index satisfies all the tortuosity properties such as invariance to translation, rotation and scaling and also the modulation properties. It is capable of differentiating the tortuosity of structures that visually appear to be different in tortuosity and shapes. The proposed index can automatically classify the image as tortuous or non tortuous. For an optimal set of training parameters, the prediction accuracy is as high as 82.94% and 86.6% on 45 retinal images at segment level and image level, respectively. The test results are verified against the judgement of two expert Ophthalmologists. The proposed index is marked by its inherent simplicity and computational attractiveness, and produces the expected estimate, irrespective of the segmentation approach. Examples and experimental results demonstrate the fitness and effectiveness of the proposed technique for both simulated curves and retinal images. key words: tortuosity, retinopathy of prematurity (ROP), principal component analysis