Automatic Location of Blood Vessel Bifurcations in Digital Eye Fundus Images

Retinal blood vessels are linked with hypertension and cardiovascular disease. It is generally known that vascular bifurcation is mainly involved in varying blood flow velocity as well as its pressure. This paper presents an efficient method for automatic location of blood vessel bifurcations in digital eye fundus images. The proposed algorithm comprised of three main steps: image enhancement, fuzzy clustering, and searching vascular bifurcation. The purposed algorithm revealed successful detection of bifurcations upon test images. Results showed improved diagnostic accuracy in identifying bifurcations with use of the proposed algorithm and encourage its use for further applications such as image registration, personal identification and pre-clinical scanning of retina diagnosis

Retinal Vasculature Identification and Characterization Using OCT Imaging

The eye fundus is the part of the human body where the blood vessels can be directly observed and studied. For this reason, the analysis and diagnosis of many relevant diseases that affect the circulatory system, for example, reference, hypertension, diabetes or arteriosclerosis can be supported by the use of this source of information, analyzing their degree of severity and impact by the study of the properties of the retinal microcirculation. The development of computer aided-diagnosis tools became relevant over the recent years as they support and facilitate the work of specialists, helping to accurately identify the target structures in many processes of analysis and diagnosis. In that sense, the automatic identification of the retinal vasculature is crucial as its manual identification is an exhaustive and tedious work when it is manually performed by the experts. This chapter presents an analysis of the characteristics of the optical coherence tomography imaging and its potential for the retinal vascular identification and characterization. In that sense, we also analyze computational approaches to automatically obtain and characterize the retinal vasculature and provide an intuitive visualization that facilitates the posterior clinical analysis of relevant diseases such as hypertension or diabetes

AUTOMATIC DETECTION AND CLASSIFICATION OF RETINAL VASCULAR LANDMARKS

The main contribution of this paper is introducing a method to distinguish between different landmarks of the retina: bifurcations and crossings. The methodology may help in differentiating between arteries and veins and is useful in identifying diseases and other special pathologies, too. The method does not need any special skills, thus it can be assimilated to an automatic way for pinpointing landmarks; moreover it gives good responses for very small vessels. A skeletonized representation, taken out from the segmented binary image (obtained through a preprocessing step), is used to identify pixels with three or more neighbors. Then, the junction points are classified into bifurcations or crossovers depending on their geometrical and topological properties such as width, direction and connectivity of the surrounding segments. The proposed approach is applied to the public-domain DRIVE and STARE datasets and compared with the state-of-the-art methods using proper validation parameters. The method was successful in identifying the majority of the landmarks; the average correctly identified bifurcations in both DRIVE and STARE datasets for the recall and precision values are: 95.4% and 87.1% respectively; also for the crossovers, the recall and precision values are: 87.6% and 90.5% respectively; thus outperforming other studies

Enhanced visualization of the retinal vasculature using depth information in OCT

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11517-017-1660-8[Abstract]: Retinal vessel tree extraction is a crucial step for analyzing the microcirculation, a frequently needed process in the study of relevant diseases. To date, this has normally been done by using 2D image capture paradigms, offering a restricted visualization of the real layout of the retinal vasculature. In this work, we propose a new approach that automatically segments and reconstructs the 3D retinal vessel tree by combining near-infrared reflectance retinography information with Optical Coherence Tomography (OCT) sections. Our proposal identifies the vessels, estimates their calibers, and obtains the depth at all the positions of the entire vessel tree, thereby enabling the reconstruction of the 3D layout of the complete arteriovenous tree for subsequent analysis. The method was tested using 991 OCT images combined with their corresponding near-infrared reflectance retinography. The different stages of the methodology were validated using the opinion of an expert as a reference. The tests offered accurate results, showing coherent reconstructions of the 3D vasculature that can be analyzed in the diagnosis of relevant diseases affecting the retinal microcirculation, such as hypertension or diabetes, among others.This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-04

Network-based features for retinal fundus vessel structure analysis

Retinal fundus imaging is a non-invasive method that allows visualizing the structure of the blood vessels in the retina whose features may indicate the presence of diseases such as diabetic retinopathy (DR) and glaucoma. Here we present a novel method to analyze and quantify changes in the retinal blood vessel structure in patients diagnosed with glaucoma or with DR. First, we use an automatic unsupervised segmentation algorithm to extract a tree-like graph from the retina blood vessel structure. The nodes of the graph represent branching (bifurcation) points and endpoints, while the links represent vessel segments that connect the nodes. Then, we quantify structural differences between the graphs extracted from the groups of healthy and non-healthy patients. We also use fractal analysis to characterize the extracted graphs. Applying these techniques to three retina fundus image databases we find significant differences between the healthy and non-healthy groups (p-values lower than 0.005 or 0.001 depending on the method and on the database). The results are sensitive to the segmentation method (manual or automatic) and to the resolution of the images.Peer ReviewedPostprint (published version

A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5×5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

Vascular Tree Tracking and Bifurcation Points Detection in Retinal Images Using a Hierarchical Probabilistic Model

Background and Objective Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases. Methods In this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation. Results The performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates. Conclusions This technique results in a classifier with high precision and recall when comparing it with Xu’s method