![]() The provided cross-sectional images are extremely useful in the identification of the different structures that are present in the human eye anatomy, such as the optic disc, the retinal vasculature, or the retinal layers. This technique uses low-coherence interferometry to produce a two-dimensional image by sequentially collecting reflections from the lateral and longitudinal scans of the retina. OCT is a non-invasive imaging technique that generates, in vivo, a cross-sectional visualization of the retinal tissues in the posterior part of the eye. In particular, in the field of ophthalmology, optical coherence tomography (OCT) plays an important role as a source of information about the retinal layers that is increasing its popularity. In modern medicine, medical imaging involves different capture technologies that are used for the visualization of the inner body parts, tissues, or organs in order to facilitate the medical diagnosis, treatments, and the corresponding clinical monitoring. These ophthalmological systems facilitate the early identification and diagnosis of different relevant pathologies and help, therefore, the doctors to make a more accurate diagnosis and treatments, reducing the consequences of incorrect or imperfect treatments as the usual side effects of unneeded medication. An accurate and robust identification of both types of vessels is a key issue in the implementation of automatic computer-aided diagnosis (CAD) systems. AVR measures the ratio between the arteriolar and venular diameters, and it is one of the most referenced metrics for the quantification of the changes in the retinal vascular structure. ![]() Among them, we can find the arterio-venular ratio (AVR). Several works studied the definition of metrics that measure the vascular morphology of the retina, particularly between arteries and veins. The analysis of these structures offers a set of biomarkers that allow the identification of several pathologies that may be present in the eye fundus, as glaucoma, diabetic retinopathy, sclerosis, or cardiovascular complications. It is composed of different types of structures whose main function is the production of visual images that are transmitted instantaneously through the optical nerve to the brain. The human eye is an anatomical part of the body that is considered as one of the most complex organs. The method achieved satisfactory results, reaching a best accuracy of 93.35% in the identification of arteries and veins, being the first proposal that faces this issue in this image modality. ![]() The methodology was validated using an OCT image dataset retrieved from 46 different patients, where 2,392 vessel segments and 97,294 vessel points were manually labeled by an expert clinician. ![]() Finally, a post-processing stage is applied to correct misclassifications using context information and maximize the performance of the classification process. A k-means clustering classifier employs these features to evaluate the potential of artery/vein identification of the proposed method. Then, Global Intensity-Based Features (GIBS) are used to measure the differences in the intensity profiles between arteries and veins. The method, firstly, segments the vessel tree and identifies its characteristic points. These scans include the near-infrared reflectance retinography images, the ones we used in this work, in combination with the corresponding histological sections. The proposed method offers a complete analysis of the retinal vascular tree structure by its identification and posterior classification into arteries and veins using optical coherence tomography (OCT) scans. An accurate identification of the retinal arteries and veins is a relevant issue in the development of automatic computer-aided diagnosis systems that facilitate the analysis of different relevant diseases that affect the vascular system as diabetes or hypertension, among others. ![]()
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