A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Automatic Identification of <s> a b s t r a c t An automated method for measurement of arteriolar-to-venular diameter ratio (AVR) is presented. The method includes optic disc segmentation for the determination of the AVR measurement zone, retinal vessel segmentation, vessel classification into arteries and veins, selection of major vessel pairs, and measurement of AVRs. The sensitivity for the major vessels in the measurement zone was 87%, while 93% of them were classified correctly into arteries or veins. In 36 out of 40 vessel pairs, at least parts of the paired vessels were correctly identified. Although the average error in the AVRs with respect to those based on the manual vessel segmentation results was 0.11, the average error in vessel diameter was less than 1 pixel. The proposed method may be useful for objective evaluation of AVRs and has a potential for detecting focal arteriolar narrowing on macula-centered screening fundus images. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Automatic Identification of <s> 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. <s> BIB002 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Automatic Identification of <s> The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification. <s> BIB003
The study of BIB002 developed an automatic technique to identify the artery and vein in illumination-corrected retinal images combining Gaussian Mixture Model, ExpectationMaximization unsupervised classifier, and a quadrantpairwise approach. The obtained specificity and precision of this technique were 0.8978 and 0.9045 for artery, respectively, and 0.9591 and 0.9408 for vein, respectively. The authors of BIB001 designed a vessel classification system employing the linear discriminant classifier in their proposed method for the measurement of AVR which gave the 92.8% accurate classification results. An automated system for the artery and vein classification was proposed in BIB003 that provides the classification result for the vasculature analyzing the intersection points (graph nodes) and assigning one of two labels to each vessel segment using graph links. The graph-based labeling results were combined with an intensity features set to perform the final classification and that showed the 89.8%, 8834%, and 87.4% accuracy for the images of VICAVR, INSPIREVR, and DRIVE database, respectively BIB003 .