Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model

Near-Infrared Forearm Vascular Width Calculation Using Radius Estimation of Tangent Circle

In response to the analysis of the functional status of forearm blood vessels, this paper fully considers the orientation of the vascular skeleton and the geometric characteristics of blood vessels and proposes a blood vessel width calculation algorithm based on the radius estimation of the tangent circle (RETC) in forearm near-infrared images. First, the initial infrared image obtained by the infrared camera is preprocessed by image cropping, contrast stretching, denoising, enhancement, and initial segmentation. Second, the Zhang-Suen refinement algorithm is used to extract the vascular skeleton. Third, the Canny edge detection method is used to perform vascular edge detection. Finally, a RETC algorithm is developed to calculate the vessel width. This paper evaluates the accuracy of the proposed RETC algorithm, and experimental results show that the mean absolute error between the vessel width obtained by our algorithm and the reference vessel width is as low as 0.36, with a variance of only 0.10, which can be significantly reduced compared to traditional calculation measurements.

Multi-modal retinal scanning to measure retinal thickness and peripheral blood vessels in multiple sclerosis

Our purpose was to investigate changes to the retina in multiple sclerosis (MS) using established and novel modes of retinal image acquisition and analysis. 72 participants with MS and 80 healthy volunteers underwent retinal scanning with optical coherence tomography (OCT) and ultra-widefield (UWF) scanning laser ophthalmoscopy (SLO), over a two-year period. Changes in retinal nerve fibre layer (RNFL) thickness, macular volume and retinal blood vessel diameter were measured and parameters were then tested for associations with MS. Measurements from OCT showed that individuals with MS had a thinner RNFL and reduced macular volume when compared to healthy volunteers. On UWF images, participants with MS had reduced arterial widths in the inferior nasal quadrant of both eyes and reduced venous widths in the inferior nasal quadrant of right eyes. Longitudinal analysis showed that participants with MS had an accelerated annual rate of RNFL thinning in several regions of the retina. In conclusion, the assessment of OCT showed thinning of the RNFL and macula in concordance with previous reports on MS, while analysis of blood vessels in the retinal periphery from UWF-SLO images revealed novel changes.

Retinal vascular measures from diabetes retinal screening photographs and risk of incident dementia in type 2 diabetes: A GoDARTS study

Objective

Patients with diabetes have an increased risk of dementia. Improved prediction of dementia is an important goal in developing future prevention strategies. Diabetic retinopathy screening (DRS) photographs may be a convenient source of imaging biomarkers of brain health. We therefore investigated the association of retinal vascular measures (RVMs) from DRS photographs in patients with type 2 diabetes with dementia risk.

Research Design and Methods

RVMs were obtained from 6,111 patients in the GoDARTS bioresource (635 incident cases) using VAMPIRE software. Their association, independent of Apo E4 genotype and clinical parameters, was determined for incident all cause dementia (ACD) and separately Alzheimer's disease (AD) and vascular dementia (VD). We used Cox's proportional hazards with competing risk of death without dementia. The potential value of RVMs to increase the accuracy of risk prediction was evaluated.

Results

Increased retinal arteriolar fractal dimension associated with increased risk of ACD (csHR 1.17; 1.08-1.26) and AD (HR 1.33; 1.16-1.52), whereas increased venular fractal dimension (FDV) was associated with reduced risk of AD (csHR 0.85; 0.74-0.96). Conversely, FDV was associated with increased risk of VD (csHR 1.22; 1.07-1.40). Wider arteriolar calibre was associated with a reduced risk of ACD (csHR 0.9; 0.83-0.98) and wider venular calibre was associated with a reduced risk of AD (csHR 0.87; 0.78-0.97). Accounting for competing risk did not substantially alter these findings. RVMs significantly increased the accuracy of prediction.

Conclusions

Conventional DRS photographs could enhance stratifying patients with diabetes at increased risk of dementia facilitating the development of future prevention strategies.

[Relationship of retinal vascular caliber with age and cardiometabolic diseases in the population over 50 years of age]

In an aging society, age-dependent diseases with high mortality, including cardiovascular diseases (CVD) and type 2 diabetes mellitus (DM2), occupy a special place. There is only limited population-based data on the relationship between cardiometabolic diseases and target-organ damage, including ocular microvasculature.

PURPOSE

To explore the associations between the caliber of retinal vessels and cardiometabolic diseases in a population sample of men and women from middle-aged to elderly (Novosibirsk).

MATERIAL AND METHODS

The subjects were participants of the Russian cohort - part of the international project HAPIEE, and were initially examined in 2003-2005 (n=9360, aged 45-69 years, Novosibirsk). At the third survey in 2015-2017, a random sub-sample of men and women (n=1011) was formed for an in-depth evaluation. We performed a calibrometric analysis involving measurement of central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), and CRAE-to-CRVE ratio (AVR).

RESULTS

In a population sample of men and women aged 55-84 years, age increment is accompanied by a decrease in the calibers of retinal arterioles and venules (p<0.001). Arterial hypertension (AH) was accompanied by a decrease in CRAE, CRVE (p=0.001) and AVR (p<0.001); the associations between AH, CRAE and AVR were independent from other factors. Multivariate analysis showed that CRAE and CRVE were inversely associated with the presence of DM2 (p=0.026). Carotid atherosclerosis was accompanied by an increase in CRVE (p<0.002); this relationship was mainly attributed to age and metabolic factors. There were no associations between carotid atherosclerosis and either CRAE or AVR. The multivariate analysis identified the weak positive associations of CRAE and AVR with the presence of ischemic heart disease and CVD.

CONCLUSION

In the examined population sample aged 55-84 years, a number of associations were detected between retinal vascular caliber and cardiometabolic diseases. The observed changes in the microvascular bed of the retina may be important for prognosis of the course of common cardiometabolic diseases.

DeepRayburst for Automatic Shape Analysis of Tree-Like Structures in Biomedical Images

Precise quantification of tree-like structures from biomedical images, such as neuronal shape reconstruction and retinal blood vessel caliber estimation, is increasingly important in understanding normal function and pathologic processes in biology. Some handcrafted methods have been proposed for this purpose in recent years. However, they are designed only for a specific application. In this paper, we propose a shape analysis algorithm, DeepRayburst, that can be applied to many different applications based on a Multi-Feature Rayburst Sampling (MFRS) and a Dual Channel Temporal Convolutional Network (DC-TCN). Specifically, we first generate a Rayburst Sampling (RS) core containing a set of multidirectional rays. Then the MFRS is designed by extending each ray of the RS to multiple parallel rays which extract a set of feature sequences. A Gaussian kernel is then used to fuse these feature sequences and outputs one feature sequence. Furthermore, we design a DC-TCN to make the rays terminate on the surface of tree-like structures according to the fused feature sequence. Finally, by analyzing the distribution patterns of the terminated rays, the algorithm can serve multiple shape analysis applications of tree-like structures. Experiments on three different applications, including soma shape reconstruction, neuronal shape reconstruction, and vessel caliber estimation, confirm that the proposed method outperforms other state-of-the-art shape analysis methods, which demonstrate its flexibility and robustness.

On the quantitative effects of compression of retinal fundus images on morphometric vascular measurements in VAMPIRE

BACKGROUND AND OBJECTIVES

This paper reports a quantitative analysis of the effects of joint photographic experts group (JPEG) image compression of retinal fundus camera images on automatic vessel segmentation and on morphometric vascular measurements derived from it, including vessel width, tortuosity and fractal dimension.

METHODS

Measurements are computed with vascular assessment and measurement platform for images of the retina (VAMPIRE), a specialized software application adopted in many international studies on retinal biomarkers. For reproducibility, we use three public archives of fundus images (digital retinal images for vessel extraction (DRIVE), automated retinal image analyzer (ARIA), high-resolution fundus (HRF)). We generate compressed versions of original images in a range of representative levels.

RESULTS

We compare the resulting vessel segmentations with ground truth maps and morphological measurements of the vascular network with those obtained from the original (uncompressed) images. We assess the segmentation quality with sensitivity, specificity, accuracy, area under the curve and Dice coefficient. We assess the agreement between VAMPIRE measurements from compressed and uncompressed images with correlation, intra-class correlation and Bland-Altman analysis.

CONCLUSIONS

Results suggest that VAMPIRE width-related measurements (central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), arteriolar-venular width ratio (AVR)), the fractal dimension (FD) and arteriolar tortuosity have excellent agreement with those from the original images, remaining substantially stable even for strong loss of quality (20% of the original), suggesting the suitability of VAMPIRE in association studies with compressed images.

Learning-based algorithms for vessel tracking: A review

Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.

Retinal Vessel Analysis as a Novel Screening Tool to Identify Childhood Acute Lymphoblastic Leukemia Survivors at Risk of Cardiovascular Disease

BACKGROUND

Microvascular endothelial dysfunction is central to the pathogenesis of cardiovascular disease (CVD). The eye offers direct access for endothelial health assessment via the retinal microvasculature. The aim of the study was to investigate whether image-based retinal vessel analysis is a feasible method of assessing endothelial health in survivors of childhood acute lymphoblastic leukemia (cALL).

MATERIALS AND METHODS

Cardiovascular risk factors (CRFs) were estimated using the 30-year Framingham Risk Score in 73 childhood leukemia survivors (median age: 25; median years from diagnosis: 19) and 78 healthy controls (median age: 23). Radial arterial stiffness was measured using pulse wave analyzer, while endothelial activation markers were measured by soluble intercellular adhesion molecule 1 (sICAM-1) and soluble vascular cell adhesion molecule 1 (sVCAM-1). Retinal fundus images were analyzed for central retinal artery/vein equivalents (CRAE/CRVE) and arteriolar-venular ratio (AVR).

RESULTS

cALL survivors had higher CRF (P<0.0001), arterial stiffness (P=0.001), and sVCAM-1 (P=0.007) compared with controls. Survivors also had significantly higher CRVE (P=0.021) while AVR was significantly lower (P=0.026) in survivors compared with controls, compatible with endothelial dysfunction. In cALL survivors with intermediate risk for CVD, CRAE, and AVR are significantly lower, while sVCAM-1 and sICAM-1 are significantly higher when compared with survivors with low CVD risk after adjusting with covariates (age, sex, and smoking status).

CONCLUSIONS

cALL survivors have an increased risk of CVD compared with age-matched peers. The survivors demonstrated microvasculopathy, as measured by retinal vascular analysis, in addition to physical and biochemical evidence of endothelial dysfunction. These changes predate other measures of CVD. Retinal vessel analysis may be utilized as a robust screening tool for identifying survivors at increased risk for developing CVD.

Examination of retinal vascular trajectory in schizophrenia and bipolar disorder

AIM

Evidence suggests microvascular dysfunction (wider retinal venules and narrower arterioles) in schizophrenia (SCZ) and bipolar disorder (BD). The vascular development is synchronous with neuronal development in the retina and brain. The retinal vessel trajectory is related to retinal nerve fiber layer thinning and cerebrovascular abnormalities in SCZ and BD and has not yet been examined. Hence, in this study we examined the retinal vascular trajectory in SCZ and BD in comparison with healthy volunteers (HV).

METHODS

Retinal images were acquired from 100 HV, SCZ patients, and BD patients, respectively, with a non-mydriatic fundus camera. Images were quantified to obtain the retinal arterial and venous trajectories using a validated, semiautomated algorithm. Analysis of covariance and regression analyses were conducted to examine group differences. A supervised machine-learning ensemble of bagged-trees method was used for automated classification of trajectory values.

RESULTS

There was a significant difference among groups in both the retinal venous trajectory (HV: 0.17 ± 0.08; SCZ: 0.25 ± 0.17; BD: 0.27 ± 0.20; P < 0.001) and the arterial trajectory (HV: 0.34 ± 0.15; SCZ: 0.29 ± 0.10; BD: 0.29 ± 0.11; P = 0.003) even after adjusting for age and sex (P < 0.001). On post-hoc analysis, the SCZ and BD groups differed from the HV on retinal venous and arterial trajectories, but there was no difference between SCZ and BD patients. The machine learning showed an accuracy of 86% and 73% for classifying HV versus SCZ and BD, respectively.

CONCLUSION

Smaller trajectories of retinal arteries indicate wider and flatter curves in SCZ and BD. Considering the relation between retinal/cerebral vasculatures and retinal nerve fiber layer thinness, the retinal vascular trajectory is a potential marker for SCZ and BD. As a relatively affordable investigation, retinal fundus photography should be further explored in SCZ and BD as a potential screening measure.

Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age

Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.

Retinal microvascular features and cognitive change in the Lothian-Birth Cohort 1936

Introduction

We test whether measures of the retinal vasculature are associated with cognitive functioning and cognitive change.

Methods

Retinal images from a narrow-age cohort were analyzed using Vessel Assessment and Measurement Platform for Images of the Retina, producing a comprehensive range of quantitative measurements of the retinal vasculature, at mean age 72.5 years (SD = 0.7). Cognitive ability and change were measured using a battery of multiple measures of memory, visuospatial, processing speed, and crystallized cognitive abilities at mean ages 73, 76, and 79 years. We applied multivariate growth curve models to test the association between retinal vascular measurements with cognitive abilities and their changes.

Results

Almost all associations were nonsignificant. In our most parsimonious model, venular asymmetry factor was associated with speed at age 73.

Discussion

Our null findings suggest that the quantitative retinal parameters applied in this study are not significantly associated with cognitive functioning or cognitive change.

A Review on the Extraction of Quantitative Retinal Microvascular Image Feature

Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. This further facilitates developments in medical imaging, enabling this robust technology to attain extensive scopes in biomedical engineering platform. Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. Apart from that, this noninvasive research tool is automated, allowing it to be used in large-scale screening programs, and all are described in this present review paper. This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. It mainly focuses on features associated with the early symptom of transient ischemic attack or sharp stroke.

Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images

Background

Changes in the retinal vessel caliber are associated with a variety of major diseases, namely diabetes, hypertension and atherosclerosis. The clinical assessment of these changes in fundus images is tiresome and prone to errors and thus automatic methods are desirable for objective and precise caliber measurement. However, the variability of blood vessel appearance, image quality and resolution make the development of these tools a non-trivial task.

Metholodogy

A method for the estimation of vessel caliber in eye fundus images via vessel cross-sectional intensity profile model fitting is herein proposed. First, the vessel centerlines are determined and individual segments are extracted and smoothed by spline approximation. Then, the corresponding cross-sectional intensity profiles are determined, post-processed and ultimately fitted by newly proposed parametric models. These models are based on Difference-of-Gaussians (DoG) curves modified through a multiplying line with varying inclination. With this, the proposed models can describe profile asymmetry, allowing a good adjustment to the most difficult profiles, namely those showing central light reflex. Finally, the parameters of the best-fit model are used to determine the vessel width using ensembles of bagged regression trees with random feature selection.

Results and conclusions

The performance of our approach is evaluated on the REVIEW public dataset by comparing the vessel cross-sectional profile fitting of the proposed modified DoG models with 7 and 8 parameters against a Hermite model with 6 parameters. Results on different goodness of fitness metrics indicate that our models are constantly better at fitting the vessel profiles. Furthermore, our width measurement algorithm achieves a precision close to the observers, outperforming state-of-the art methods, and retrieving the highest precision when evaluated using cross-validation. This high performance supports the robustness of the algorithm and validates its use in retinal vessel width measurement and possible integration in a system for retinal vasculature assessment.

Towards Standardization of Quantitative Retinal Vascular Parameters: Comparison of SIVA and VAMPIRE Measurements in the Lothian Birth Cohort 1936

Purpose

Semiautomated software applications derive quantitative retinal vascular parameters from fundus camera images. However, the extent of agreement between measurements from different applications is unclear. We evaluate the agreement between retinal measures from two software applications, the Singapore "I" Vessel Assessment (SIVA) and the Vessel Assessment and Measurement Platform for Images of the Retina (VAMPIRE), and examine respective associations between retinal and systemic outcomes.

Method

Fundus camera images from 665 Lothian Birth Cohort 1936 participants were analyzed with SIVA and VAMPIRE. Intraclass correlation coefficients (ICC) and Bland-Altman plots assessed agreement between retinal parameters: measurements of vessel width, fractal dimension, and tortuosity. Retinal-systemic variable associations were assessed with Pearson's correlation, and intersoftware correlation magnitude differences were examined with Williams's test.

Results

ICC values indicated poor to limited agreement for all retinal parameters (0.159-0.410). Bland-Altman plots revealed proportional bias in the majority, and systematic bias in all measurements. SIVA and VAMPIRE measurements were associated most consistently with systemic variables relating to blood pressure (SIVA r's from -0.122 to -0.183; VAMPIRE r's from -0.078 to -0.177). Williams's tests indicated significant differences in the magnitude of association between retinal and systemic variables for 7 of 77 comparisons (P < 0.05).

Conclusions

Agreement between two common software applications was poor. Further studies are required to determine whether associations with systemic variables are software-dependent.

Translational Relevance

Standardization of the measurement of retinal vascular parameters is warranted to ensure that they are reliable and application-independent. This would be an important step towards realizing the potential of the retina as a source of imaging-derived biomarkers that are clinically useful.

A review on automatic analysis techniques for color fundus photographs

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.

Modulation of retinal image vasculature analysis to extend utility and provide secondary value from optical coherence tomography imaging

Retinal image analysis is emerging as a key source of biomarkers of chronic systemic conditions affecting the cardiovascular system and brain. The rapid development and increasing diversity of commercial retinal imaging systems present a challenge to image analysis software providers. In addition, clinicians are looking to extract maximum value from the clinical imaging taking place. We describe how existing and well-established retinal vasculature segmentation and measurement software for fundus camera images has been modulated to analyze scanning laser ophthalmoscope retinal images generated by the dual-modality Heidelberg SPECTRALIS(®) instrument, which also features optical coherence tomography.

Association between retinal vasculature and muscle mass in older people

Sarcopenia in older people is a major health issue and its early detection could help target interventions and improve health. Evidence suggests that poor muscle mass is associated with greater arterial stiffness and cardiovascular risk. Arterial stiffness in turn is associated with smaller retinal artery width. This study examined the association of muscle mass in older people with retinal vascular width, a non-invasive measure of vascular function.

METHODS

Participants >65 years were recruited to a cross-sectional study.

EXCLUSIONS

Inability to walk independently; diabetes mellitus; stroke (within 6 months), severe macular degeneration, glaucoma, retinal dystrophy; advanced cataract. Digital Retinal images of both eyes were analysed using the VAMPIRE software suite. Central Retinal Artery and Vein Equivalents (CRVE and CRAE) were measured. Body composition was measured using Dual Energy X ray Absorptimetry (DXA). Appendicular Skeletal Muscle Mass/Height(2) was calculated. Physical function was measured: 6-min walk distance, Short Physical performance battery, handgrip strength and quadriceps strength.

RESULTS

79 participants with mean age 72 (SD 6) years were recruited. 44% were female. Digital Retinal images of sufficient quality for measuring CRAE and CRVE were available for 51/75 (68%) of participants. Regression analysis showed significant association between larger ASMM/H(2) and smaller CRAE (β=-0.20, p=0.001) and CRVE (β=-0.12, p=0.05). Handgrip strength, body mass index and sex combined with CRAE explained 88% and with CRVE explained 86% of the variance in ASMM/H(2).

CONCLUSION

Larger muscle mass was significantly associated with smaller retinal artery size in older people. This unexpected finding needs further investigation.

Automated identification of retinal vessels using a multiscale directional contrast quantification (MDCQ) strategy

PURPOSE

A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs.

METHODS

The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, we tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy.

RESULTS

For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%.

CONCLUSIONS

The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.

Unsupervised recognition of retinal vascular junction points

Landmark points in retinal images can be used to create a graph representation to understand and to diagnose not only different pathologies of the eye, but also a variety of more general diseases. Aim of this paper is the description of a non-supervised methodology to distinguish between bifurcations and crossings of the retinal vessels, which can be used in differentiating between arteries and veins. A thinned representation of the binarized image, is used to identify pixels with three or more neighbors. Junction points are classified into bifurcations or crossovers according to their geometrical and topological properties. The proposed approach is successfully compared with the state-of-the-art methods with the benchmarks DRIVE and STARE. The recall, precision and F-score average detection values are 91.5%, 88.8% and 89.8% respectively.

Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy

Features of the retinal vasculature, such as vessel widths, are considered biomarkers for systemic disease. The aim of this work is to present a supervised approach to vessel segmentation in ultra-wide field of view scanning laser ophthalmoscope (UWFoV SLO) images and to evaluate its performance in terms of segmentation and vessel width estimation accuracy. The results of the proposed method are compared with ground truth measurements from human observers and with existing state-of-the-art techniques developed for fundus camera images that we optimized for UWFoV SLO images. Our algorithm is based on multi-scale matched filters, a neural network classifier and hysteresis thresholding. After spline-based refinement of the detected vessel contours, the vessel widths are estimated from the binary maps. Such analysis is performed on SLO images for the first time. The proposed method achieves the best results, both in vessel segmentation and in width estimation, in comparison to other automatic techniques.

Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions

The black void behind the pupil was optically impenetrable before the invention of the ophthalmoscope by von Helmholtz over 150 years ago. Advances in retinal imaging and image processing, especially over the past decade, have opened a route to another unexplored landscape, the retinal neurovascular architecture and the retinal ganglion pathways linking to the central nervous system beyond. Exploiting these research opportunities requires multidisciplinary teams to explore the interface sitting at the border between ophthalmology, neurology and computing science. It is from the detail and depth of retinal phenotyping that novel metrics and candidate biomarkers are likely to emerge. Confirmation that in vivo retinal neurovascular measures are predictive of microvascular change in the brain and other organs is likely to be a major area of research activity over the next decade. Unlocking this hidden potential within the retina requires integration of structural and functional data sets, that is, multimodal mapping and longitudinal studies spanning the natural history of the disease process. And with further advances in imaging, it is likely that this area of retinal research will remain active and clinically relevant for many years to come. Accordingly, this review looks at state-of-the-art retinal imaging and its application to diagnosis, characterization and prognosis of chronic illness or long-term conditions.

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE).