Automatic Retinal Vessel Tortuosity Measurement

Abbadi, Nidhal K. El; Saadi, Enas Hamood Al

doi:10.3844/jcssp.2013.1456.1460

Cited by 12 publications

(4 citation statements)

References 7 publications

(7 reference statements)

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“…Gelman et al [ 22 ] applied this approach to specially selected images related to ROP cases, and Grisman et al [ 23 ] improved the arc to chord measure by taking into consideration the amplitude of the curve and number of turns. Nidhal Khdhair El Abbadi and Enas Hamood Al Saadi [ 24 ] also used the arc to chord measure for the segmentation of vasculature. They used a mask filter over the length of a BV branch to follow each segment of the retina’s blood vessels.…”

Section: Retinal Tortuosity Reviewmentioning

confidence: 99%

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

et al. 2022

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Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.

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Section: Retinal Tortuosity Reviewmentioning

confidence: 99%

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

et al. 2022

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“…This area is represented by a white color spectrum. Non-zero pixels are preserved by a union-find table [8].…”

Section: Related Workmentioning

confidence: 99%

Premature Infant Blood Vessel Segmentation of Retinal Images Based on Hybrid Method for the Determination of Tortuosity

et al. 2020

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For the retinal blood vessels segmentation, we used a method, which is based on the morphological operations. The output of this process is extracted retinal binary image, where is situated main blood vessels. In this paper is used dataset of images (2800 images) from device RetCam3. Before applying the image processing, it was selected 30 images with diagnosed pre-plus diseases, and it is divided into two groups with low contrast and good contrast images. In the next part of the analysis, it was analyzing and showing blood vessels with tortuosity. Tortuosity is a symptom of ROP (retinopathy of prematurity). The clinical physicians evaluate tortuosity by visual comparison of the retinal images images. For this reason, it was suggested model which can automatically indicate the tortuosity of the retinal blood vessels by setting a threshold of the blood vessels curvature.

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“…Following this, the authors achieved the best sensitivity and classification rate of 97.8% and 93.6% for non-tortuous, semi-tortuous, and extreme tortuous vessel segments, respectively. El Abbadi and Al Saadi 16 proposed a method for automatic human retinal vessel tortuosity measurement. The authors used a mask filter to trace the vessel along its course; tortuosity was then evaluated using the arc-to-chord ratio.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The histogram plot for a moderately tortuous vessel segment is shown in Figure 11b. In this case, the maximum occurrence of blood vessel pixels in a given row increases to 16 and the number of rows containing the maximum number of vessel pixels increases to 18. The plot consists of more than one flat region.…”

Section: Tortuosity Measurement By Counting the Occurrence Of Vessel Pixelsmentioning

confidence: 99%

Image processing-based model for tortuosity measurement of retinal blood vessels

Chetia

Nirmala

2019

MAIO

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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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Automatic Retinal Vessel Tortuosity Measurement

Cited by 12 publications

References 7 publications

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image

Premature Infant Blood Vessel Segmentation of Retinal Images Based on Hybrid Method for the Determination of Tortuosity

Image processing-based model for tortuosity measurement of retinal blood vessels

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