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Hypertensive Retinopathy is an ocular disease that occurs due to the presence of hypertension in the body. Hypertensive Retinopathy is quantified using a parameter known as Arteriovenous Ratio. To calculate Arteriovenous Ratio, retinal blood vessels in fundus images are segmented, classified, and measured. The complex structure of retinal vessels and uneven illumination in fundus images make retinal vessel classification a difficult task. In this paper, we propose a method for an improved differentiation of retinal vessels, that employs Binary Particle Swarm Optimization to select the optimal retinal vessel features. We design an objective function that considers the size and relevancy of the feature subset in classifying the retinal vessels. The designed objective function ensures the selected feature subset contains the minimum number of features and it corresponds to maximum vessel classification accuracy. Once, the optimization process has reached the stopping criteria, the selected feature subset is evaluated using Support Vector Machines. We compare the retinal vessel classification accuracy obtained using the proposed framework with the existing state-of-the-art approaches and found our method more accurate and robust on healthy as well as diseased images. The effectiveness of the proposed method is validated using the public as well as a private dataset. [ABSTRACT FROM AUTHOR]

Summary: The performance of active contour model is limited on retinal vessel segmentation as vessel images are usually corrupted with intensity inhomogeneity, low contrast, and weak boundary, which severely affect the segmentation results of retinal vessels. A new active contour model combining the local and global information is proposed in this paper to facilitate the vessel segmentation. In our model, the fuzzy conception is firstly introduced as fuzzy methods generally provide more accurate and robust clustering and the concept of fuzziness in fuzzy clustering, which is represented by membership, can reflect the intensity distribution of the image. Then, we define local energy based on Maximum a Posterior Probability and use spatially varying parameters, mean and stand deviation, to describe the local Gaussian distribution in order to better deal with intensity inhomogeneity. Furthermore, we combine local and global energy based on fuzzy clustering, with a weight coefficient. The coefficient is computed by a weight function according to contrast ratio of the image. Experiments on synthetic and real images and comparisons with other state‐of‐the‐art active contour models show that the proposed model can detect objects more accurate and robust, especially for vessels on retinal angiogram. [ABSTRACT FROM AUTHOR]

This paper attempts to estimate diagnostically relevant measure, i.e., Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers decision-combination scheme. The features exploited for retinal vessel characterization are based on statistical measures of histogram, different filter responses of images and local gradient information. The feature selection process is based on two feature ranking approaches (Pearson Correlation Coefficient technique and Relief-F method) to rank the features followed by use of maximum classification accuracy of three supervised classifiers (k-Nearest Neighbor, Support Vector Machine and Naïve Bayes) as a threshold for feature subset selection. Retinal vessels are labeled using the selected feature subset and proposed hybrid classification scheme, i.e., decision fusion of multiple classifiers. The comparative analysis shows an increase in vessel classification accuracy as well as Arteriovenous Ratio calculation performance. The system is tested on three databases, a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies. Overall, an accuracy of 90.45%, 93.90% and 87.82% is achieved in retinal blood vessel separation with 0.0565, 0.0650 and 0.0849 mean error in Arteriovenous Ratio calculation for Local, INSPIRE-AVR and VICAVR dataset, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate a comprehensive method for segmenting the retinal vasculature in camera images of the fundus. This is of interest in the area of diagnostics for eye diseases that affect the blood vessels in the eye. In a departure from other state-of-the-art methods, vessels are first pre-grouped together with graph partitioning, using a spectral clustering technique based on morphological features. Local curvature is estimated over the whole image using eigenvalues of Hessian matrix in order to enhance the vessels, which appear as ridges in images of the retina. The result is combined with a binarized image, obtained using a threshold that maximizes entropy, to extract the retinal vessels from the background. Speckle type noise is reduced by applying a connectivity constraint on the extracted curvature based enhanced image. This constraint is varied over the image according to each region's predominant blood vessel size. The resultant image exhibits the central light reflex of retinal arteries and veins, which prevents the segmentation of whole vessels. To address this, the earlier entropy-based binarization technique is repeated on the original image, but crucially, with a different threshold to incorporate the central reflex vessels. The final segmentation is achieved by combining the segmented vessels with and without central light reflex. We carry out our approach on DRIVE and REVIEW, two publicly available collections of retinal images for research purposes. The obtained results are compared with state-of-the-art methods in the literature using metrics such as sensitivity (true positive rate), selectivity (false positive rate) and accuracy rates for the DRIVE images and measured vessel widths for the REVIEW images. Our approach out-performs the methods in the literature. [ABSTRACT FROM AUTHOR]