D. Relan

Retinal vessel classification: Sorting arteries and veins

For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an Expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in [12] [14] and [15]. The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in [13]. The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.

Novel VAMPIRE algorithms for quantitative analysis of the retinal vasculature

This paper summarizes three recent, novel algorithms developed within VAMPIRE, namely optic disc and macula detection, arteryvein classification, and enhancement of binary vessel masks, and their performance assessment. VAMPIRE is an international collaboration growing a suite of software tools to allow efficient quantification of morphological properties of the retinal vasculature in large collections of fundus camera images. VAMPIRE measurements are currently mostly used in biomarker research, i.e., investigating associations between the morphology of the retinal vasculature and a number of clinical and cognitive conditions.

Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE

It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate.

Retinal Vessel Classification Based on Maximization of Squared-Loss Mutual Information

The classification of retinal vessels into arterioles and venules is important for any automated system for the detection of vascular changes in the retina and for the discovery of biomarkers associated with systemic diseases such as diabetes, hypertension, and cardiovascular disease. We introduce Squared-loss Mutual Information clustering (SMIC) for classifying arterioles and venules in retinal images for the first time (to the best of our knowledge). We classified vessels from 70 fundus camera images using only 4 colour features in zone B (802 vessels) and in an extended zone (1,207 vessels). We achieved an accuracy of 90.67 and 87.66 % in zone B and the extended zone, respectively. We further validated our algorithm by classifying vessels in zone B from two publically available datasets—INSPIRE-AVR (483 vessels from 40 images) and DRIVE (171 vessels from 20 test images). The classification rates obtained on INSPIRE-AVR and DRIVE dataset were 87.6 and 86.2 %, respectively. We also present a technique to sort the unclassified vessels which remained unlabeled by the SMIC algorithm.

Using orthogonal locality preserving projections to find dominant features for classifying retinal blood vessels

Automatically classifying retinal blood vessels appearing in fundus camera imaging into arterioles and venules can be problematic due to variations between people as well as in image quality, contrast and brightness. Using the most dominant features for retinal vessel types in each image rather than predefining the set of characteristic features prior to classification may achieve better performance. In this paper, we present a novel approach to classifying retinal vessels extracted from fundus camera images which combines an Orthogonal Locality Preserving Projections for feature extraction and a Gaussian Mixture Model with Expectation-Maximization unsupervised classifier. The classification rate with 47 features (the largest dimension tested) using OLPP on our own ORCADES dataset and the publicly available DRIVE dataset was 90.56%

Novel Locus Influencing Retinal Venular Tortuosity Is Also Associated With Risk Of Coronary Artery Disease

90.56\%

Morphometric Measurements of The Retinal Vasculature in Fundus Images with Vampire

and 86.7%

OF THE RETINAL VASCULATURE IN FUNDUS IMAGES WITH VAMPIRE

86.7\%

Proc. SICSA Dundee Medical Analysis Workshop

respectively.

Proc. SICSA Dundee Medical Image Analysis Workshop

Structural variation in retinal blood vessels is associated with global vascular health in humans and may provide a readily accessible indicator of several diseases of vascular origin. We report a meta-analysis of genome-wide association studies (GWAS) for quantitative retinal vascular traits derived using semi-automatic image analysis of digital retinal photographs from the GoDARTS (n=1736) and ORCADES (n=1358) cohorts. We identified a novel genome-wide significant locus at 19q13 (ACTN4/CAPN12) for retinal venular tortuosity, and one at 13q34 (COL4A2) for retinal arteriolar tortuosity; these two loci were subsequently confirmed in three independent cohorts. In the combined analysis, the lead SNP at each locus was rs1808382 in ACTN4/CAPN12 (P=2.39×10−13) and rs7991229 in COL4A2 (P=4.66×10−12). Notably, the ACTN4/CAPN12 locus associated with retinal venular tortuosity traits is also associated with coronary artery disease and heart rate. Our findings demonstrate the contribution of genetics in retinal vascular traits, and provide new insights into vascular diseases.