Uyen T. V. Nguyen

An effective retinal blood vessel segmentation method using multi-scale line detection

Changes in retinal blood vessel features are precursors of serious diseases such as cardiovascular disease and stroke. Therefore, analysis of retinal vascular features can assist in detecting these changes and allow the patient to take action while the disease is still in its early stages. Automation of this process would help to reduce the cost associated with trained graders and remove the issue of inconsistency introduced by manual grading. Among different retinal analysis tasks, retinal blood vessel extraction plays an extremely important role as it is the first essential step before any measurement can be made. In this paper, we present an effective method for automatically extracting blood vessels from colour retinal images. The proposed method is based on the fact that by changing the length of a basic line detector, line detectors at varying scales are achieved. To maintain the strength and eliminate the drawbacks of each individual line detector, the line responses at varying scales are linearly combined to produce the final segmentation for each retinal image. The performance of the proposed method was evaluated both quantitatively and qualitatively on three publicly available DRIVE, STARE, and REVIEW datasets. On DRIVE and STARE datasets, the proposed method achieves high local accuracy (a measure to assess the accuracy at regions around the vessels) while retaining comparable accuracy compared to other existing methods. Visual inspection on the segmentation results shows that the proposed method produces accurate segmentation on central reflex vessels while keeping close vessels well separated. On REVIEW dataset, the vessel width measurements obtained using the segmentations produced by the proposed method are highly accurate and close to the measurements provided by the experts. This has demonstrated the high segmentation accuracy of the proposed method and its applicability for automatic vascular calibre measurement. Other advantages of the proposed method include its efficiency with fast segmentation time, its simplicity and scalability to deal with high resolution retinal images.

iVAT and aVAT: enhanced visual analysis for cluster tendency assessment

Given a pairwise dissimilarity matrix D of a set of n objects, visual methods (such as VAT) for cluster tendency assessment generally represent D as an n×n image

An Automated Method for Retinal Arteriovenous Nicking Quantification From Color Fundus Images

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Automated quantification of retinal arteriovenous nicking from colour fundus images

where the objects are reordered to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is the inability to highlight cluster structure in

Automatic detection of retinal vascular landmark features for colour fundus image matching and patient longitudinal study

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An effective supervised framework for retinal blood vessel segmentation using local standardisation and bagging

when D contains highly complex clusters. To address this problem, this paper proposes an improved VAT (iVAT) method by combining a path-based distance transform with VAT. In addition, an automated VAT (aVAT) method is also proposed to automatically determine the number of clusters from

A Novel Path-Based Clustering Algorithm Using Multi-dimensional Scaling

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A Randomized Path Routing Algorithm for Decentralized Route Allocation in Transportation Networks

. Experimental results on several synthetic and real-world data sets have demonstrated the effectiveness of our methods.

An effective automated system for grading severity of retinal arteriovenous nicking in colour retinal images.

Retinal arteriovenous (AV) nicking is one of the prominent and significant microvascular abnormalities. It is characterized by the decrease in the venular caliber at both sides of an artery-vein crossing. Recent research suggests that retinal AV nicking is a strong predictor of eye diseases such as branch retinal vein occlusion and cardiovascular diseases such as stroke. In this study, we present a novel method for objective and quantitative AV nicking assessment. From the input retinal image, the vascular network is first extracted using the multiscale line detection method. The crossover point detection method is then performed to localize all AV crossing locations. At each detected crossover point, the four vessel segments, two associated with the artery and two associated with the vein, are identified and two venular segments are then recognized through the artery-vein classification method. The vessel widths along the two venular segments are measured and analyzed to compute the AV nicking severity of that crossover. The proposed method was validated on 47 high-resolution retinal images obtained from two population-based studies. The experimental results indicate a strong correlation between the computed AV nicking values and the expert grading with a Spearman correlation coefficient of 0.70. Sensitivity was 77% and specificity was 92% (Kappa κ = 0.70) when comparing AV nicking detected using the proposed method to that detected using a manual grading method, performed by trained photographic graders.

Retinal vascular feature analysis using color fundus imaging

Retinal arteriovenous nicking (AV nicking) is the phenomenon where the venule is compressed or decreases in its caliber at both sides of an arteriovenous crossing. Recent research suggests that retinal AVN is associated with hypertension and cardiovascular diseases such as stroke. In this article, we propose a computer method for assessing the severity level of AV nicking of an artery-vein (AV) crossing in color retinal images. The vascular network is first extracted using a method based on multi-scale line detection. A trimming process is then performed to isolate the main vessels from unnecessary structures such as small branches or imaging artefact. Individual segments of each vessel are then identified and the vein is recognized through an artery-vein identification process. A vessel width measurement method is devised to measure the venular caliber along its two segments. The vessel width measurements of each venular segment is then analyzed and assessed separately and the final AVN index of a crossover is computed as the most severity of its two segments. The proposed technique was validated on 69 AV crossover points of varying AV nicking levels extracted from retinal images of the Singapore Malay Eye Study (SiMES). The results show that the computed AVN values are highly correlated with the manual grading with a Spearman correlation coefficient of 0.70. This has demonstrated the accuracy of the proposed method and the feasibility to develop a computer method for automatic AV nicking detection. The quantitative measurements provided by the system may help to establish a more reliable link between AV nicking and known systemic and eye diseases, which deserves further examination and exploration.