Vessels In Retinal Images Research Articles

High Resolution Imaging of Retinal Vessels as a Biomarker of Cardiovascular Disease in Patients With Renal Insufficiency - RI

High Resolution Imaging of Retinal Vessels as a Biomarker of Cardiovascular Disease in Patients With Renal Insufficiency - RI

Retinal Blood Vessel Segmentation with Improved Convolutional Neural Networks

Retinal blood vessel feature is one of crucial biomarkers for ophthalmologic and cardiovascular diseases, efficiency image segmentation technologies will help doctors diagnose these related diseases. We propose an improved deep CNN model to segment retinal blood vessels. Our method includes three steps: Data augmentation, Image preprocessing methods and Model training. The data augmentation uses the rotation and image mirroring to make the training image better generalization. The CLAHE algorithm is used for image preprocessing, which can reduce the image noise and enhance tiny retinal blood vessels features. Finally, we used a deep CNN model combined with U-Net and Dense-Net structure to train retinal blood vessel image. The result of proposed model was tested on public available dataset DRIVE, achieving an average accuracy 0.951, specificity 0.973, sensitivity 0.797 and the average AUC is 0.885. The results show its potential for clinical application.

Blood vessel detection based on fractional Hessian matrix with non-singular Mittag–Leffler Gaussian kernel

In this work, we proposed a novel method based on fractional Gaussian kernel involving the Atangana–Baleanu fractional derivative to detect blood vessels in retinal images. This method is based on the fractional Gaussian matrix eigenvalues analysis to obtain the principal curvature. The non-integer orders of the fractional Gaussian mask for retinal image denoising and the fractional Gaussian matrix were optimized by Cuckoo Search algorithm to get more accurate results. To evaluate the effectiveness and the robustness of the method it was compared with manual segmentation and other works reported in the literature.

A hybrid method for blood vessel segmentation in images

In the last years, image processing has been an important tool for health care. The analysis of retinal vessel images has become crucial to achieving a better diagnosis and treatment for several cardiovascular and ophthalmological deceases. Therefore, an automatic and accurate procedure for retinal vessel and optic disc segmentation is essential for illness detection. This task is extremely hard and time-consuming, often requiring the assistance of human experts with a high degree of professional skills. Several retinal vessel segmentation methods have been developed with satisfactory results. Nevertheless, most of such techniques present a poor performance mainly due to the complex structure of vessels in retinal images. In this paper, an accurate methodology for retinal vessel and optic disc segmentation is presented. The proposed scheme combines two different techniques: the Lateral Inhibition (LI) and the Differential Evolution (DE). The LI scheme produces a new image with enhanced contrast between the background and retinal vessels. Then, the DE algorithm is used to obtain the appropriate threshold values through the minimization of the cross-entropy function from the enhanced image. To evaluate the performance of the proposed approach, several experiments over images extracted from STARE, DRIVE, and DRISHTI-GS databases have been conducted. Simulation results demonstrate a high performance of the proposed scheme in comparison with similar methods reported in the literature.

Retinal Image Vessel Width Analysis in Retinopathy of Prematurity

This paper aims at development of Oscillating Pendulum Based Algorithm (OPBA) for retinal image width computation and analysis for the investigation of Retinopathy of prematurity (ROP). The algorithms have been explained in detail with theoretical investigations and simulations, images from local hospital data base were considered in the investigation. Further this paper also presents an overview of direct retinal vessel width computation method along with the comparative analysis. The results obtained are found to be encouraging for analysis of plus disease under ROP.

Retinal oximetry: Metabolic imaging for diseases of the retina and brain.

Retinal oximetry imaging of retinal blood vessels measures oxygen saturation of hemoglobin. The imaging technology is non-invasive and reproducible with remarkably low variability on test-retest studies and in healthy cohorts. Pathophysiological principles and novel biomarkers in several retinal diseases have been discovered, as well as possible applications for systemic and brain disease. In diabetic retinopathy, retinal venous oxygen saturation is elevated and arteriovenous difference progressively reduced in advanced stages of retinopathy compared with healthy persons. This correlates with pathophysiology of diabetic retinopathy where hypoxia stimulates VEGF production. Laser treatment and vitrectomy both improve retinal oximetry values, which correlate with clinical outcome. The oximetry biomarker may allow automatic measurement of severity of diabetic retinopathy and predict its response to treatment. Central retinal vein occlusion is characterized by retinal hypoxia, which is evident in retinal oximetry. The retinal hypoxia seen on oximetry correlates with the extent of peripheral ischemia, visual acuity and thickness of macular edema. This biomarker may help diagnose and measure severity of vein occlusion and degree of retinal ischemia. Glaucomatous retinal atrophy is associated with reduced oxygen consumption resulting in reduced arteriovenous difference and higher retinal venous saturation. The oximetry findings correlate with worse visual field, thinner nerve fiber layer and smaller optic disc rim. This provides an objective biomarker for glaucomatous damage. In retinitis pigmentosa, an association exists between advanced atrophy, worse visual field and higher retinal venous oxygen saturation, lower arteriovenous difference. This biomarker may allow measurement of severity and progression of retinitis pigmentosa and other atrophic retinal diseases. Retinal oximetry offers visible light imaging of systemic and central nervous system vessels. It senses hypoxia in cardiac and pulmonary diseases. Oximetry biomarkers have been discovered in Alzheimer's disease and multiple sclerosis and oxygen levels in the retina correspond well with brain.

Contrast Agent Enhanced Multimodal Photoacoustic Microscopy and Optical Coherence Tomography for Imaging of Rabbit Choroidal and Retinal Vessels in vivo

Multimodal imaging with photoacoustic microscopy (PAM) and optical coherence tomography (OCT) can be an effective method to evaluate the choroidal and retinal microvasculature. To improve the efficiency for visualizing capillaries, colloidal gold nanoparticles (AuNPs) have been applied as a multimodal contrast agent for both OCT and PAM imaging by taking advantage of the strong optical scattering and the strong optical absorption of AuNPs due to their surface plasmon resonance. Ultra-pure AuNPs were fabricated by femtosecond laser ablation, capped with polyethylene glycol (PEG), and administered to 13 New Zealand white rabbits and 3 Dutch Belted pigmented rabbits. The synthesized PEG-AuNPs (20.0 ± 1.5 nm) were demonstrated to be excellent contrast agents for PAM and OCT, and do not demonstrate cytotoxicity to bovine retinal endothelial cells in cell studies. The image signal from the retinal and choroidal vessels in living rabbits was enhanced by up to 82% for PAM and up to 45% for OCT, respectively, by the administered PEG-AuNPs, which enables detection of individual blood vessels by both imaging modalities. The biodistribution study demonstrated the AuNP accumulated primarily in the liver and spleen. Histology and TUNEL staining did not indicate cell injury or death in the lung, liver, kidney, spleen, heart, or eyes up to seven days after AuNP administration. PEG-AuNPs offer an efficient and safe contrast agent for multimodal ocular imaging to achieve improved characterization of microvasculature.

Optical coherence tomography angiography in neuronal diseases : Preliminary findings

Optical coherence tomography angiography (OCTA) enables a noninvasive detailed imaging of retinal and choroidal vessels of the fundus. In neuronal diseases changes in retinal structures can be imaged and measured with OCT and OCTA. Can OCTA be used in neuronal diseases? Evaluation of recent scientific articles and studies extracted from Medline on the topic of OCTA and neuronal diseases. It could be shown that Alzheimer type dementia, Parkinson's disease, multiple sclerosis. cerebral infarction and CADASIL are neuronal diseases with rarification of retinal vessels and atrophy of the retinal layers in the ocular fundus. These findings are beyond all changes which can be appreciated with ophthalmoscopy and OCTA parameters could serve in the future as supplementary biomarkers for assessment of the retinal-neurovascular coupling in these diseases.

Width measurement for pathological vessels in retinal images using centerline correction and k-means clustering

Changes in retinal blood vessel width and morphology are closely related to the development of many diseases. However, some vessels (especially pathological vessels) often show central light reflex in retinal images, making it difficult to detect the vessel boundary and to measure the vessel width. In this study, a measuring methodology is proposed to improve the performance in measuring blood vessels with central light reflex, by use of centerline correction and k-means clustering algorithm. Firstly, the vessel tree in the retinal image is segmented using Isotropic Undecimated Wavelet Transform. Then, the centerline of each vessel is roughly extracted using morphological thinning algorithm, and corrected for vessels with central light reflex by use of a parallel line model. With known centerline, the direction and length of the measuring axis are determined, and the intensity profile of the pixels along the axis is obtained. By use of k-means clustering algorithm for the intensities, the pixels on the measuring axis are classified into vessel and background, based on which the vessel width is finally determined. The proposed method is tested on the public dataset REVIEW and our own dataset, and compared with ground truth and state-of-the-art method (ARIA method). It is shown that the measuring accuracy of proposed method is comparable to ARIA (average relative error: 3% vs. 4%), but it is more advantageous in measuring pathological vessels with central light reflex. It succeeds in measuring all the testing pathological vessels whereas ARIA is capable of measuring 20% of them. Result of the work has potential usage in quantitative evaluation of the progressing of retinal diseases.

Semantic segmentation of artery-venous retinal vessel using simple convolutional neural network

Semantic segmentation is how to categorize objects in an image based on pixel color intensity. There is an implementation in the medical imaging. This article discusses semantic segmentation in retinal blood vessels. Retinal blood vessels consist of artery and vein. Arteryvenous segmentation is needed to detect diabetic retinopathy, hypertension, and artherosclerosis. The data for the experiment is Retinal Image vessel Tree Extraction (RITE). Data consists of 20 patches with a dimension of 128 × 128 × 3. The process for performing semantic segmentation consists of 3 method, create a Conventional Neural Network (CNN) model, pre-trained network, and training the network. The CNN model consists of 10 layers, 1 input layer image, 3 convolution layers, 2 Rectified Linear Units (ReLU), 1 Max pooling, 1 transposed convolution layer, 1 softmax and 1 pixel classification layer. The pre-trained network uses the optimization algorithm Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square Propagation (RMSProp) and Adaptive Moment optimization (Adam). Various scenarios were tested to get optimal accuracy. The learning rate is 1e-3 and 1e-2. Minibatch size are 4,8,16,32,64, and 128. The maximum value of epoch is set to 100. The results show the highest accuracy of up to 98.35%

Segmentation of retinal image vessels based on fully convolutional network with depthwise separable convolution and channel weighting

Diseases such as diabetes and hypertension can lead to change the shape of the retinal blood vessels. Segmentation of fundus images is a key step in the process of quantitative analysis of the disease, which is instructive in the analysis and diagnosis of clinical diseases. In this paper, a method for the segmentation of retinal image vessels based on fully convolutional network (FCN) with depthwise separable convolution and channel weighting is presented. Firstly, CLAHE and Gamma correction of the green channel of the fundus image are used to enhance the contrast. Then, in order to adapt to network training, the enhanced image is divided into patches to expand the data. Finally, the depthwise separable convolution instead of the standard convolution method is used to increase the network width. Meanwhile, the channel weighting module is introduced to explicitly model the relationship between the characteristic channels in order to improve the distinguishability of the features. The combination of them is applied to the FCN and the results of expert manual identification are used to supervise the experiment on the DRIVE database. The results show that the segmentation accuracy of the proposed method in DRIVE database reached 0.963 0 and AUC reached 0.983 1. The segmentation accuracy in STARE database reached 0.962 0 and AUC achieved 0.983 0. To some extent, the proposed method has better feature resolution and better segmentation performance.

A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images.

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect terminations for tree-like structures based on a multiscale ray-shooting model and a termination visual prior. The multiscale ray-shooting model detects 2D terminations by extracting and analyzing the multiscale intensity distribution features around a termination candidate. The range of scale is adaptively determined according to the local neurite diameter estimated by the Rayburst sampling algorithm in combination with the gray-weighted distance transform. The termination visual prior is based on a key observation-when observing a 3D termination from three orthogonal directions without occlusion, we can recognize it in at least two views. Using this prior with the multiscale ray-shooting model, we can detect 3D terminations with high accuracies. Experiments on 3D neuron image stacks, 2D neuron images, 3D bronchus image stacks, and 2D retinal blood vessel images exhibit average precision and recall rates of 87.50% and 90.54%. The experimental results confirm that the proposed method outperforms other the state-of-the-art termination detection methods.

Short- and Long-Term Effects of Bariatric Surgery on Vascular Phenotype.

Retinal microvascular diameters and large artery stiffness are valid biomarkers of cardiovascular risk. This study assessed short- and long-term micro- and macrovascular improvements after bariatric surgery (BS). Sixteen patients (44 ± 12years) underwent BS in this observational study. Two weeks before as well as 6weeks and 4years after surgery, retinal vessel analysis and assessment of brachial-ankle pulse wave velocity (baPWV), cardio-ankle vascular index (CAVI), and anthropometry were performed. Three patients were lost to follow-up. Six weeks after BS, retinal arteriolar diameters (CRAE) were wider (180.1μm vs. 188.1μm; p = 0.001), and the arteriolar-to-venular diameter ratio (AVR) was higher (0.82 vs. 0.86; p < 0.001) compared to baseline levels. During the 4years of follow-up, the retinal changes sustained but further improvements did not occur. Both indices of large artery stiffness, baPWV and CAVI, remained unchanged 6weeks and 4years after surgery. Retinal microvascular phenotype improved 6weeks after BS. The improvements in microvascular health were maintained during 4years of follow-up but, despite significant further reductions in body mass index, did not improve further long-term. baPWV and CAVI were unaffected after surgery indicating that BS primarily affects microvascular phenotype rather than large artery stiffness. Retinal vessel imaging seems to be a feasible diagnostic tool to monitor microvascular health after BS. Normalization of BMI and blood pressure may be necessary to achieve long-term improvement of large artery phenotype after BS.

Blood Vessels Segmentation in Retinal Fundus Image using Hybrid Method of Frangi Filter, Otsu Thresholding and Morphology

Diagnosis of computer-based retinopathic hypertension is done by analyzing of retinal images. The analysis is carried out through various stages, one of which is blood vessel segmentation in retinal images. Vascular segmentation of the retina is a complex problem. This is caused by non-uniform lighting, contrast variations and the presence of abnormalities due to disease. This makes segmentation not successful if it only relies on one method. The aims of this study to segment blood vessels in retinal images. The method used is divided into three stages, namely preprocessing, segmentation and testing. The first stage, preprocessing, is to improve image quality with the CLAHE method and the median filter on the green channel image. The second stage, segmenting using a number of methods, namely, frangi filter, 2D-convolution filtering, median filtering, otsu's thresholding, morphology operation, and background subtraction. The last step is testing the system using the DRIVE and STARE dataset. The test results obtained sensitivity 91.187% performance parameters, 86.896% specificity, and area under the curve (AUC) 89.041%. Referring to the performance produced, the proposed model can be used as an alternative for blood vessel segmentation of retinal images.

Micro-Vessel Image Segmentation Based on the AD-UNet Model

Retinal vessel segmentation plays a vital role in computer-aided diagnosis and treatment of retinal diseases. Considering the low contrast between retinal vessels and the background image, complex structural information as well as blurred boundaries between tissue and blood vessels, the retinal vessel image segmentation algorithm based on the improved U-Net network is proposed in the paper. The algorithm introduces an attention mechanism and densely connected network into the original U-Net network and realizes the automatic segmentation of retinal vessels. According to the test results of the algorithm on commonly-used datasets of the DRIVE and STARE fundus images, respectively, the accuracy is 0.9663 and 0.9684; the sensitivity is 0.8075 and 0.8437; the specificity is 0.9814 and 0.9762; the AUC values are 0.9846 and 0.9765; and the F-measures are 0.8203 and 0.8419, respectively. In the paper, the Attention-Dense-UNet (AD-UNet) algorithm is applied to segment human bulbar conjunctival micro-vessels. The experimental results show that the algorithm can achieve ideal segmentation results.

The multiscale bowler-hat transform for blood vessel enhancement in retinal images

Enhancement, followed by segmentation, quantification and modelling of blood vessels in retinal images plays an essential role in computer-aided retinopathy diagnosis. In this paper, we introduce the bowler-hat transform method a new approach based on mathematical morphology for vessel enhancement. The proposed method combines different structuring elements to detect innate features of vessel-like structures. We evaluate the proposed method qualitatively and quantitatively and compare it with the state-of-the-art methods using both synthetic and real datasets. Our results establish that the proposed method achieves high-quality vessel-like structure enhancement in both synthetic examples and clinically relevant retinal images. The bowler-hat transform is shown to be able to detect fine vessels while still remaining robust at junctions.

Minimal Paths for Tubular Structure Segmentation With Coherence Penalty and Adaptive Anisotropy.

The minimal path method has proven to be particularly useful and efficient in tubular structure segmentation applications. In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term. The features that characterize the appearance and anisotropy properties of a tubular structure are extracted through the associated orientation score. The proposed the dynamic Riemannian metric is updated in the course of the geodesic distance computation carried out by the efficient single-pass fast marching method. Compared to the state-of-the-art minimal path models, the proposed minimal path model is able to extract the desired tubular structures from a complicated vessel tree structure. In addition, we propose an efficient prior path-based method to search for vessel radius value at each centerline position of the target. Finally, we perform the numerical experiments on both synthetic and real images. The quantitive validation is carried out on retinal vessel images. The results indicate that the proposed model indeed achieves a promising performance.

Association of cardiorespiratory fitness with retinal vessel diameters as a biomarker of cardiovascular risk

ObjectiveWe aimed to investigate the association of retinal microvascular health with cardiorespiratory fitness (VO2peak) and cardiovascular risk factors. MethodsIn a population of 260 obesity-enriched participants we investigated the association of retinal vessel diameters with cardiorespiratory fitness (CRF), body mass index (BMI) and blood pressure (BP). Retinal vessel imaging was performed by use of a fundus camera and a semi-automated processing software, calculating the central retinal arteriolar (CRAE) and venular equivalent (CRVE) as well as the arteriolar-to-venular diameter ratio (AVR). ResultsParticipants had a mean age of 45.8 ± 12.5 years and a BMI of 35.8 ± 6.8 kg/m2. 45% of patients were diagnosed with hypertension, 26% with diabetes and 30% with dyslipidemia. Increasing VO2peak was independently associated with lower CRVE (β = −0.600; CI −1.141, −0.060; p = 0.030). Higher BMI and mean arterial pressure were independently associated with narrower CRAE (β = −0.492; CI −0.909, −0.076; p = 0.021 and β = −0.268; CI −0.471, −0.066; p = 0.009, respectively) and lower AVR (β = −0.002; CI −0.003, −0.000; p = 0.026 and β = −0.001; CI −0.002, −0.000; p = 0.001, respectively). ConclusionsHigher cardiorespiratory fitness is associated with beneficial retinal microvascular health. Higher BMI and BP were associated with an impairment of retinal microvascular health. Exercise is known for its potential to improve body composition and reduce BP but may also prove to be an efficient therapy to counteract small vessel disease in cardiometabolic disease.

Obesity, Blood Pressure, and Retinal Vessels: A Meta-analysis.

Retinal vessel imaging is a noninvasive diagnostic tool used to evaluate cardiovascular risk. Childhood obesity and elevated blood pressure (BP) are associated with retinal microvascular alterations. To systematically review and meta-analyze associations between obesity, BP, and physical activity with retinal vessel diameters in children. We conducted a literature search through the databases of PubMed, Embase, Ovid, Web of Science, and the Cochrane Register of Controlled Trials. School- and population-based cross-sectional data. General information, study design, participants, exposure, and outcomes. A total of 1751 studies were found, and 30 full-text articles were analyzed for eligibility. Twenty-two articles (18 865 children and adolescents) were used for further assessment and reflection. Eleven articles were finally included in the meta-analysis. We found that a higher BMI is associated with narrower retinal arteriolar (pooled estimate effect size -0.37 [95% confidence interval (CI): -0.50 to -0.24]) and wider venular diameters (0.35 [95% CI: 0.07 to 0.63]). Systolic and diastolic BP are associated with retinal arteriolar narrowing (systolic BP: -0.63 [95% CI: -0.92 to -0.34]; diastolic BP: -0.60 [95% CI -0.95 to -0.25]). Increased physical activity and fitness are associated with favorable retinal vessel diameters. Long-term studies are needed to substantiate the prognostic relevance of retinal vessel diameters for cardiovascular risk in children. Our results indicate that childhood obesity, BP, and physical inactivity are associated with retinal microvascular abnormalities. Retinal vessel diameters seem to be sensitive microvascular biomarkers for cardiovascular risk stratification in children.

A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images

Abstract Background and objective Accurate segmentation of retinal vessels from color fundus images play a significant role in early diagnosis of various ocular, systemic and neuro-degenerative diseases. Segmenting retinal vessels is challenging due to varying nature of vessel caliber, the proximal presence of pathological lesions, strong central vessel reflex and relatively low contrast images. Most existing methods mainly rely on carefully designed hand-crafted features to model the local geometrical appearance of vasculature structures, which often lacks the discriminative capability in segmenting vessels from a noisy and cluttered background. Methods We propose a novel visual attention guided unsupervised feature learning (VA-UFL) approach to automatically learn the most discriminative features for segmenting vessels in retinal images. Our VA-UFL approach captures both the knowledge of visual attention mechanism and multi-scale contextual information to selectively visualize the most relevant part of the structure in a given local patch. This allows us to encode a rich hierarchical information into unsupervised filtering learning to generate a set of most discriminative features that aid in the accurate segmentation of vessels, even in the presence of cluttered background. Results Our proposed method is validated on the five publicly available retinal datasets: DRIVE, STARE, CHASE_DB1, IOSTAR and RC-SLO. The experimental results show that the proposed approach significantly outperformed the state-of-the-art methods in terms of sensitivity, accuracy and area under the receiver operating characteristic curve across all five datasets. Specifically, the method achieved an average sensitivity greater than 0.82, which is 7% higher compared to all existing approaches validated on DRIVE, CHASE_DB1, IOSTAR and RC-SLO datasets, and outperformed even second-human observer. The method is shown to be robust to segmentation of thin vessels, strong central vessel reflex, complex crossover structures and fares well on abnormal cases. Conclusions The discriminative features learned via visual attention mechanism is superior to hand-crafted features, and it is easily adaptable to various kind of datasets where generous training images are often scarce. Hence, our approach can be easily integrated into large-scale retinal screening programs where the expensive labelled annotation is often unavailable.