Retinal vascular tree morphology: a semi-automatic quantification

Slope Chain Code-based scale-independent tortuosity measurement on retinal vessels

Retinal vascular tortuosity presents valuable potential as a clinical biomarker for many relevant vascular and systemic diseases. Our work exhibits twofold: first, the definition of a novel scale-invariant metric to measure retinal blood vessel tortuosity; and second, the generation of a local database, called SCALE-TORT, with the intention of providing a means to test the scale invariance property on real retinal vessels rather than on synthetic data. The proposed scale invariant tortuosity metric is based on the Extended Slope Chain Code which uses variable straight-line segments for describing curves. It is focused on the representation of high-definition curves, the length of the segments is a function of the slope changes of the curve. Scale invariance is an important property when several different retinal image settings or different acquisition sources are used during a particular study or in clinical practice. The database SCALE-TORT, introduced herein, was built semi-automatically from digital images containing the coordinates of blood vessel central lines (curves) taken from images of the same eye obtained by two different imaging methodologies: retinal fundus camera and scanning laser ophthalmoscope. The vessel curves extracted from the same eye are paired for images acquired by the fundus camera and those acquired by the scanning laser ophthalmoscope to evaluate the scale invariance of the metric. Ten different tortuosity metrics were implemented and compared including our proposed metric. Three experiments were conducted to test the metrics and their properties. The first aimed to determine which tortuosity metrics possess the following properties: scale invariance, sensitivity to sudden tortuosity changes when the curve remains constant in size, and how they behave when curves are concatenated. In the second experiment, all reviewed metrics were tested on the publicly available RET-TORT database, to compare the results of the specific metric with the tortuosity classification provided by their experts and in comparison with other authors. Finally, in the third experiment, the behavior of different metrics, including those which are scale-invariant, were tested by utilizing the paired retinal vessel curves from our new SCALE-TORT database. In comparison with other tortuosity metrics, we show that the metric Extended Slope Chain Code proposed in this work optimally complies with scale invariance, in addition to having sufficient sensitivity to detect abrupt changes in tortuosity. Easy implementation being a further plus. Furthermore, we present a new and valuable database for scale property evaluation on images of retinal blood vessels called SCALE-TORT. As far as we are aware, there is no public database with these characteristics.

A software for quantitative measurement of vessel parameters in fundus images

Retinal vessel is a unique structure that allows non-invasive observation of the microcirculatory system. Its pathological features and abnormal structural alterations are associated with cardiovascular and systemic diseases. Especially the abnormalities in caliber features, histology features, and geometric structure of retinal vessels are indicative of these diseases. However, the complex distribution and imperceptible characteristics of vasculature have hindered the measurement of vessel parameters. To this end, we design a new software (Retinal Vessel Parameters Quantitative Measurement Software, RVPQMS) to quantitatively measure the features of retinal vessels. The RVPQMS is designed with the functions of vessel segmentation, landmark localization, vessel tracking, vessel identification and parameter measurement. It enables comprehensive measurement of vessel parameters in both standard zone and whole area. To ensure the accuracy of the software, the algorithms integrated in this software are validated on both private and public datasets, and experimental results demonstrate that it has excellent performance in vessel segmentation, tracking and identification. The RVPQMS software provides thorough and quantitative measurement of retinal vessel parameters, facilitating the study of vessel features for cardiovascular and systemic diseases.

A cognitive deep learning approach for medical image processing

In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.

The RETA Benchmark for Retinal Vascular Tree Analysis

Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.

Generation of Anatomically Inspired Human Airway Tree Using Electrical Impedance Tomography: A Method to Estimate Regional Lung Filling Characteristics

The purpose of lung recruitment is to improve and optimize the air exchange flow in the lungs by adjusting the respiratory settings during mechanical ventilation. Electrical impedance tomography (EIT) is a monitoring tool that permits measurement of regional pulmonary filling characteristics or filling index (FI) during ventilation. The conventional EIT system has limitations which compromise the accuracy of the FI. This paper proposes a novel and automated methodology for accurate FI estimation based on EIT images of recruitable regional collapse and hyperdistension during incremental positive end-expiratory pressure. It identifies details of the airway tree (AT) to generate a correction factor to the FIs providing an accurate measurement. Multi-scale image enhancement followed by identification of the AT skeleton with a robust and self-exploratory tracing algorithm is used to automatically estimate the FI. AT tracing was validated using phantom data on a ground-truth lung. Based on generated phantom EIT images, including an established reference, the proposed method results in more accurate FI estimation of 65% in all quadrants compared with the current state-of-the-art. Measured regional filling characteristics were also examined by comparing regional and global impedance variations in clinically recorded data from ten different subjects. Clinical tests on filling characteristics based on extraction of the AT from the resolution enhanced EIT images indicated a more accurate result compared with the standard EIT images.

A novel multiple subdivision-based algorithm for quantitative assessment of retinal vascular tortuosity

Vascular tortuosity as an indicator of retinal vascular morphological changes can be quantitatively analyzed and used as a biomarker for the early diagnosis of relevant disease such as diabetes. While various methods have been proposed to evaluate retinal vascular tortuosity, the main obstacle limiting their clinical application is the poor consistency compared with the experts' evaluation. In this research, we proposed to apply a multiple subdivision-based algorithm for the vessel segment vascular tortuosity analysis combining with a learning curve function of vessel curvature inflection point number, emphasizing the human assessment nature focusing not only global but also on local vascular features. Our algorithm achieved high correlation coefficients of 0.931 for arteries and 0.925 for veins compared with clinical grading of extracted retinal vessels. For the prognostic performance against experts' prediction in retinal fundus images from diabetic patients, the area under the receiver operating characteristic curve reached 0.968, indicating a good consistency with experts' predication in full retinal vascular network evaluation.

Efficient 3D Junction Detection in Biomedical Images Based on a Circular Sampling Model and Reverse Mapping

Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87.11% and 88.33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.

Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data

BACKGROUND

Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, highlighting the need for fresh prospects on the prescreening and in-hospital treatment strategies.

METHODS

Through two cross-sectional studies, we adopt image recognition techniques to identify pre-disease aortic morphology for prior diagnoses; assuming that AD has occurred, we employ functional data analysis to determine the optimal timing for BP and HR interventions to offer the highest possible survival rate.

RESULTS

Compared with the healthy control group, the aortic centerline is significantly more slumped for the AD group. Further, controlling patients' blood pressure and heart rate according to the likelihood of adverse events can offer the highest possible survival probability.

CONCLUSIONS

The degree of slumpness is introduced to depict aortic morphological changes comprehensively. The morphology-based prediction model is associated with an improvement in the predictive accuracy of the prescreening of AD. The dynamic model reveals that blood pressure and heart rate variations have a strong predictive power for adverse events, confirming this model's ability to improve AD management.

Quantifying neurovascular canal branching patterns reveals a shared crocodylian arrangement

Highly branched dendritic structures are common in nature and often difficult to quantify and therefore compare. Cranial neurovascular canals, examples of such structures, are osteological correlates for somatosensory systems and have been explored only qualitatively. Adaptations of traditional stream-ordering methods are applied to representative structures derived from computed tomography-scan data. Applying these methods to crocodylian taxa, this clade demonstrates a shared branching pattern and exemplifies the comparative utility of these methods. Additionally, this pattern corresponds with current understanding of crocodylian sensory abilities and behaviors. The method is applicable to many taxa and anatomical structures and provides evidence for morphology-based hypotheses of sensory and physiological evolution.

Written in Blood: Applying Shape Grammars to Retinal Vasculatures

Purpose

Blood vessel networks within the retina are crucial for maintaining tissue perfusion and therefore good vision. Their complexity and unique patterns often require a steep learning curve for humans to identify trends and changes in the shape and topology of the networks, even though there exists much information important to identifying disease within them.

Methods

Through image processing, the vasculature is isolated from other features of the fundus images, forcing the viewer to focus on the complex vascular feature. This article explores an approach using a grammar based on shape to describe retinal vasculature and to generate realistic and increasingly unrealistic artificial vascular networks that are then reviewed by ophthalmologists via digital survey. The ophthalmologists are asked whether these artificial vascular networks appeared realistic or unrealistic.

Results

With only three rules (initiate, branch, and curve), the grammar accomplishes these goals. Networks are generated by adding noise to rule parameters present in existing networks. Via the survey of synthetic networks generated with different noise parameters, a correlation between noise in the branch rule and realistic association is revealed.

Conclusions

By creating a language to describe retinal vasculature, this article allows for the potential of new insight into such an important but less understood feature of the retina, which in the future may play a role in diagnosing or helping to predict types of ocular disease.

Translational Relevance

Applying shape grammar to describe retinal vasculature permits new understanding, which in turn provides the potential for new diagnostic tools.

Infrared retinal images for flashless detection of macular edema

This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (A_Z) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

Morphological Analysis of Retinal Microvasculature to Improve Understanding of Retinal Hemorrhage Mechanics in Infants

Purpose

In this experimental study, we quantify retinal microvasculature morphological features with depth, region, and age in immature and mature ovine eyes. These data identify morphological vulnerabilities in young eyes to inform the mechanics of retinal hemorrhage in children.

Methods

Retinal specimens from the equator and posterior pole of preterm (n = 4) and adult (n = 9) sheep were imaged using confocal microscopy. Vessel segment length, diameter, angular asymmetry, tortuosity, and branch points were quantified using a custom image segmentation code. Significant differences were identified through two-way ANOVAs and correlation analyses.

Results

Vessel segment lengths were significantly shorter in immature eyes compared to adults (P < 0.003) and were significantly shorter at increasing depths in the immature retina (P < 0.04). Tortuosity significantly increased with depth, regardless of age (P < 0.05). These data suggest a potential vulnerability of vasculature in the deeper retinal layers, particularly in immature eyes. Preterm retina had significantly more branch points than adult retina in both the posterior pole and equator, and the number increased significantly with depth (P < 0.001).

Conclusions

The increased branch points and decreased segment lengths in immature microvasculature suggest that infants will experience greater stress and strain during traumatic loading compared to adults. The increased morphological vulnerability of the immature microvasculature in the deeper layers of the retina suggest that intraretinal hemorrhages have a greater likelihood of occurring from trauma compared to preretinal hemorrhages. The morphological features captured in this study lay the foundation to explore the mechanics of retinal hemorrhage in infants and identify vulnerabilities that explain patterns of retinal hemorrhage in infants.

Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast, and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization, and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, namely INSPIRE, IOSTAR, VICAVR, DRIVE, and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community.

Retinal vascular junction detection and classification via deep neural networks

BACKGROUND AND OBJECTIVES

The retinal fundus contains intricate vascular trees, some of which are mutually intersected and overlapped. The intersection and overlapping of retinal vessels represent vascular junctions (i.e. bifurcation and crossover) in 2D retinal images. These junctions are important for analyzing vascular diseases and tracking the morphology of vessels. In this paper, we propose a two-stage pipeline to detect and classify the junction points.

METHODS

In the detection stage, a RCNN-based Junction Proposal Network is utilized to search the potential bifurcation and crossover locations directly on color retinal images, which is followed by a Junction Refinement Network to eliminate the false detections. In the classification stage, the detected junction points are identified as crossover or bifurcation using the proposed Junction Classification Network that shares the same model structure with the refinement network.

RESULTS

Our approach achieves 70% and 60% F1-score on DRIVE and IOSTAR dataset respectively which outperform the state-of-the-art methods by 4.5% and 1.7%, with a high and balanced precision and recall values.

CONCLUSIONS

This paper proposes a new junction detection and classification method which performs directly on color retinal images without any vessel segmentation nor skeleton preprocessing. The superior performance demonstrates that the effectiveness of our approach.

An automatic evaluation method for retinal image registration based on similar vessel structure matching

Registration of retinal images is significant for clinical diagnosis. Numerous methods have been proposed to evaluate registration performance. The available evaluation methods can work well in normal image pairs, but fair evaluation cannot be obtained for image pairs with anatomical changes. We propose an automatic method to quantitatively assess the registration of retinal images based on the extraction of similar vessel structures and modified Hausdorff distance. Firstly, vessel detection and skeletonization are performed to detect the vascular centerline. Secondly, the vessel segments having similar structures in the image pair are selected for assessment of registration. The bifurcation and terminal points are determined from the vascular centerline. Then, the Hungarian matching algorithm with a pruning process is employed to match the bifurcation and terminal points to detect similar vessel segments. Finally, a modified Hausdorff distance is employed to evaluate the performance of registration. Our experimental results show that the Pearson product-moment correlation coefficient can reach 0.76 and 0.63 in test set of normal image pairs and image pairs with anomalies respectively, which outperforms other methods. An accurate evaluation can not only compare the performance of different registration methods but also can facilitate the clinical diagnosis by screening out the inaccurate registration. Graphical abstract .

Resonances in the response of fluidic networks inherent to the cooperation between elasticity and bifurcations

A global response function (GRF) of an elastic network is introduced as a generalization of the response function (RF) of a rigid network, relating the average flow along the network with the pressure difference at its extremes. The GRF can be used to explore the frequency behaviour of a fluid confined in a tree-like symmetric elastic network in which vessels bifurcate into identical vessels. We study such dynamic response for elastic vessel networks containing viscous fluids. We find that the bifurcation structure, inherent to tree-like networks, qualitatively changes the dynamic response of a single elastic vessel, and gives resonances at certain frequencies. This implies that the average flow throughout the network could be enhanced if the pulsatile forcing at the network's inlet were imposed at the resonant frequencies. The resonant behaviour comes from the cooperation between the bifurcation structure and the elasticity of the network, since the GRF has no resonances either for a single elastic vessel or for a rigid network. We have found that resonances shift to high frequencies as the system becomes more rigid. We have studied two different symmetric tree-like network morphologies and found that, while many features are independent of network morphology, particular details of the response are morphology dependent. Our results could have applications to some biophysical networks, for which the morphology could be approximated to a tree-like symmetric structure and a constant pressure at the outlet. The GRF for these networks is a characteristic of the system fluid-network, being independent of the dynamic flow (or pressure) at the network's inlet. It might therefore represent a good quantity to differentiate healthy vasculatures from those with a medical condition. Our results could also be experimentally relevant in the design of networks engraved in microdevices, since the limit of the rigid case is almost impossible to attain with the materials used in microfluidics and the condition of constant pressure at the outlet is often given by the atmospheric pressure.

Engineering the vasculature for islet transplantation

The microvasculature in the pancreatic islet is highly specialized for glucose sensing and insulin secretion. Although pancreatic islet transplantation is a potentially life-changing treatment for patients with insulin-dependent diabetes, a lack of blood perfusion reduces viability and function of newly transplanted tissues. Functional vasculature around an implant is not only necessary for the supply of oxygen and nutrients but also required for rapid insulin release kinetics and removal of metabolic waste. Inadequate vascularization is particularly a challenge in islet encapsulation. Selectively permeable membranes increase the barrier to diffusion and often elicit a foreign body reaction including a fibrotic capsule that is not well vascularized. Therefore, approaches that aid in the rapid formation of a mature and robust vasculature in close proximity to the transplanted cells are crucial for successful islet transplantation or other cellular therapies. In this paper, we review various strategies to engineer vasculature for islet transplantation. We consider properties of materials (both synthetic and naturally derived), prevascularization, local release of proangiogenic factors, and co-transplantation of vascular cells that have all been harnessed to increase vasculature. We then discuss the various other challenges in engineering mature, long-term functional and clinically viable vasculature as well as some emerging technologies developed to address them. The benefits of physiological glucose control for patients and the healthcare system demand vigorous pursuit of solutions to cell transplant challenges. STATEMENT OF SIGNIFICANCE: Insulin-dependent diabetes affects more than 1.25 million people in the United States alone. Pancreatic islets secrete insulin and other endocrine hormones that control glucose to normal levels. During preparation for transplantation, the specialized islet blood vessel supply is lost. Furthermore, in the case of cell encapsulation, cells are protected within a device, further limiting delivery of nutrients and absorption of hormones. To overcome these issues, this review considers methods to rapidly vascularize sites and implants through material properties, pre-vascularization, delivery of growth factors, or co-transplantation of vessel supporting cells. Other challenges and emerging technologies are also discussed. Proper vascular growth is a significant component of successful islet transplantation, a treatment that can provide life-changing benefits to patients.

Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation

PURPOSE

Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries/veins classification are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases.

METHODS

We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A nonlocal total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel-based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins.

RESULTS

The proposed segmentation method yields competitive results on three public data sets (STARE, DRIVE, and IOSTAR), and it has superior performance when compared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to five public databases (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries/veins classification based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively.

CONCLUSIONS

The experimental results show that the proposed framework has effectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology reconstruction. The vascular topology information significantly improves the accuracy on arteries/veins classification.

Retinal vascular changes in preterm infants: heart and lung diseases and plus disease

PURPOSE

To report the retinal vascular features of preterm infants with congenital heart disease (CHD), lung disease (pulmonary hypertension [PH] and bronchopulmonary dysplasia [BPD]), and ROP with plus disease to determine whether these disease entities are distinguishable on the basis of retinal vessel morphology.

METHODS

The medical records of preterm infants with CHD, lung disease, and ROP with plus disease were reviewed retrospectively. Qualitative vascular findings were validated using computer-based software to analyze 25 representative images, each corresponding to one infant's eye. The images were organized into five groups, based on clinical information. Vessel diameter (d) and tortuosity index (TI) were measured.

RESULTS

A total of 106 infants (mean gestational age, 30.5 ± 2.22 weeks) were initially included. Ophthalmologic evaluation of preterm infants with CHD and lung diseases showed vascular tortuosity without vasodilation at the posterior pole as well as in the periphery. Quantitative analysis showed that venular diameter was significantly increased in the plus disease group (P = 0.0022) compared to other groups. There was significantly less tortuosity in both arterioles and venules in BPD (P < 0.001, P = 0.0453) compared with plus group.

CONCLUSIONS

The patterns of retinal vascular tortuosity observed in preterm infants may be unique to different systemic congestive conditions and could have therapeutic implications.

Retinopathy of Prematurity-assist: Novel Software for Detecting Plus Disease

Purpose

To design software with a novel algorithm, which analyzes the tortuosity and vascular dilatation in fundal images of retinopathy of prematurity (ROP) patients with an acceptable accuracy for detecting plus disease.

Methods

Eighty-seven well-focused fundal images taken with RetCam were classified to three groups of plus, non-plus, and pre-plus by agreement between three ROP experts. Automated algorithms in this study were designed based on two methods: the curvature measure and distance transform for assessment of tortuosity and vascular dilatation, respectively as two major parameters of plus disease detection.

Results

Thirty-eight plus, 12 pre-plus, and 37 non-plus images, which were classified by three experts, were tested by an automated algorithm and software evaluated the correct grouping of images in comparison to expert voting with three different classifiers, k-nearest neighbor, support vector machine and multilayer perceptron network. The plus, pre-plus, and non-plus images were analyzed with 72.3%, 83.7%, and 84.4% accuracy, respectively.

Conclusions

The new automated algorithm used in this pilot scheme for diagnosis and screening of patients with plus ROP has acceptable accuracy. With more improvements, it may become particularly useful, especially in centers without a skilled person in the ROP field.

An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image

(BACKGROUND AND OBJECTIVES)

Retinal artery and vein classification is an important task for the automatic computer-aided diagnosis of various eye diseases and systemic diseases. This paper presents an improved supervised artery and vein classification method in retinal image.

(METHODS)

Intra-image regularization and inter-subject normalization is applied to reduce the differences in feature space. Novel features, including first-order and second-order texture features, are utilized to capture the discriminating characteristics of arteries and veins.

(RESULTS)

The proposed method was tested on the DRIVE dataset and achieved an overall accuracy of 0.923.

(CONCLUSION)

This retinal artery and vein classification algorithm serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases.

Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity

Retinopathy of prematurity (ROP), a disorder of the retina occurring in preterm infants, is the leading cause of preventable childhood blindness. An active phase of ROP that requires treatment is associated with the presence of plus disease, which is diagnosed clinically in a qualitative manner by visual assessment of the existence of a certain level of increase in the thickness and tortuosity of retinal vessels. The present study performs computer-aided diagnosis (CAD) of plus disease via quantitative measurement of tortuosity in retinal fundus images of preterm infants. Digital image processing techniques were developed for the detection of retinal vessels and measurement of their tortuosity. The total lengths of abnormally tortuous vessels in each quadrant and the entire image were then computed. A minimum-length diagnostic-decision-making criterion was developed to assess the diagnostic sensitivity and specificity of the values obtained. The area ([Formula: see text]) under the receiver operating characteristic curve was used to assess the overall diagnostic accuracy of the methods. Using a set of 19 retinal fundus images of preterm infants with plus disease and 91 without plus disease, the proposed methods provided an overall diagnostic accuracy of [Formula: see text]. Using the total length of all abnormally tortuous vessel segments in an image, our techniques are capable of CAD of plus disease with high accuracy without the need for manual selection of vessels to analyze. The proposed methods may be used in a clinical or teleophthalmological setting.

Automated construction of arterial and venous trees in retinal images

While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixel-wise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input.

Computer-aided diagnosis of plus disease in retinal fundus images of preterm infants via measurement of vessel tortuosity

An increase in retinal vessel tortuosity can be indicative of the presence of various diseases including retinopathy of prematurity (ROP). Accurate detection and measurement of such changes could help in computer-aided diagnosis of plus disease, which warrants treatment of ROP. We present image processing methods for detection and segmentation of retinal vessels, quantification of vessel tortuosity, and diagnostic-decision-making criteria that incorporate the clinical definition of plus-diagnosis. The obtained results using 110 retinal fundus images of preterm infants (91 without plus and 19 with plus) provide high sensitivity = 0.89 (17/19) and excellent specificity = 0.95 (86/91) in the diagnosis of plus disease.

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.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis.

A Novel Curvature-Based Algorithm for Automatic Grading of Retinal Blood Vessel Tortuosity

Tortuosity of retinal blood vessels is an important symptom of diabetic retinopathy or retinopathy of prematurity. In this paper, we propose an automatic image-based method for measuring single vessel and vessel network tortuosity of these vessels. Simplicity of the algorithm, low-computational burden, and an excellent matching to the clinically perceived tortuosity are the important features of the proposed algorithm. To measure tortuosity, we use curvature which is an indicator of local inflection of a curve. For curvature calculation, template disk method is a common choice and has been utilized in most of the state of the art. However, we show that this method does not possess linearity against curvature and by proposing two modifications, we improve the method. We use the basic and the modified methods to measure tortuosity on a publicly available data bank and two data banks of our own. While interpreting the results, we pursue three goals. First, to show that our algorithm is more efficient to implement than the state of the art. Second, to show that our method possesses an excellent correlation with subjective results (0.94 correlation for vessel tortuosity, 0.95 correlation for vessel network tortuosity in diabetic retinopathy, and 0.7 correlation for vessel network tortuosity in retinopathy of prematurity). Third, to show that the tortuosity perceived by an expert and curvature possess a nonlinear relation.

Computer-aided diagnosis software for hypertensive risk determination through fundus image processing

The goal of the software proposed in this paper is to assist ophthalmologists in diagnosis and disease prevention, helping them to determine cardiovascular risk or other diseases where the vessels can be altered, as well as to monitor the pathology progression and response to different treatments. The performance of the tool has been evaluated by means of a double-blind study where its sensitivity, specificity, and reproducibility to discriminate between health fundus (without cardiovascular risk) and hypertensive patients has been calculated in contrast to an expert ophthalmologist opinion obtained through a visual inspection of the fundus image. An improvement of almost 20% has been achieved comparing the system results with the clinical visual classification.

An automatic graph-based approach for artery/vein classification in retinal images

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.

Plus disease diagnosis in retinopathy of prematurity: vascular tortuosity as a function of distance from optic disk

PURPOSE

To examine vascular tortuosity as a function of distance from the optic disk in infants with retinopathy of prematurity.

METHODS

Thirty-four wide-angle retinal images from infants with retinopathy of prematurity were reviewed by 22 experts. A reference standard for each image was defined as the diagnosis (plus vs. not plus) given by the majority of experts. Tortuosity, defined as vessel length divided by straight line distance between vessel end points, was calculated as a function of distance from the disk margin for arteries and veins using computer-based methods developed by the authors.

RESULTS

Mean cumulative tortuosity increased with distance from the disk margin, both in 13 images with plus disease (P = 0.007 for arterial tortuosity [n = 62 arteries], P < 0.001 for venous tortuosity [n = 58 veins] based on slope of best fit line by regression), and in 21 images without plus disease (P < 0.001 for arterial tortuosity [n = 94 arteries], P <0 .001 for venous tortuosity [n = 85 veins]). Images with plus disease had significantly higher vascular tortuosity than images without plus disease (P < 0.05), up to 7.0 disk diameters from the optic disk margin.

CONCLUSION

Vascular tortuosity was higher peripherally than centrally, both in images with and without plus disease, suggesting that peripheral retinal features may be relevant for retinopathy of prematurity diagnosis.

Tracing retinal vessel trees by transductive inference

BACKGROUND

Structural study of retinal blood vessels provides an early indication of diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. These studies require accurate tracing of retinal vessel tree structure from fundus images in an automated manner. However, the existing work encounters great difficulties when dealing with the crossover issue commonly-seen in vessel networks.

RESULTS

In this paper, we consider a novel graph-based approach to address this tracing with crossover problem: After initial steps of segmentation and skeleton extraction, its graph representation can be established, where each segment in the skeleton map becomes a node, and a direct contact between two adjacent segments is translated to an undirected edge of the two corresponding nodes. The segments in the skeleton map touching the optical disk area are considered as root nodes. This determines the number of trees to-be-found in the vessel network, which is always equal to the number of root nodes. Based on this undirected graph representation, the tracing problem is further connected to the well-studied transductive inference in machine learning, where the goal becomes that of properly propagating the tree labels from those known root nodes to the rest of the graph, such that the graph is partitioned into disjoint sub-graphs, or equivalently, each of the trees is traced and separated from the rest of the vessel network. This connection enables us to address the tracing problem by exploiting established development in transductive inference. Empirical experiments on public available fundus image datasets demonstrate the applicability of our approach.

CONCLUSIONS

We provide a novel and systematic approach to trace retinal vessel trees with the present of crossovers by solving a transductive learning problem on induced undirected graphs.

The mouse retina in 3D: quantification of vascular growth and remodeling

The mouse retina has become a prominent model for studying angiogenesis. The easy access and well-known developmental progression have significantly propelled our ability to examine and manipulate blood vessels in vivo. Nonetheless, most studies have restricted their evaluations to the superficial plexus (an upper vascular layer in contact with the vitreous). Here we present experimental data and quantification for the developmental progression of the full retina including the intermediate and deeper plexus that sprouts from the superficial layer. We analyze the origin and advancement of vertical sprouting and present the progression of vascular perfusion within the tissue. Furthermore, we introduce the use of Minkowsky functionals to quantify remodeling in the superficial and deeper plexus. The work expands information on the retina towards a 3D structure. This is of particular interest, as recent data have demonstrated differential effects of gene deletion on the upper and deeper plexus, highlighting the concept of distinct operational pathways during sprouting angiogenesis.

Computational model for oxygen transport and consumption in human vitreous

PURPOSE

Previous studies that measured liquefaction and oxygen content in human vitreous suggested that exposure of the lens to excess oxygen causes nuclear cataracts. Here, we developed a computational model that reproduced available experimental oxygen distributions for intact and degraded human vitreous in physiologic and environmentally perturbed conditions. After validation, the model was used to estimate how age-related changes in vitreous physiology and structure alter oxygen levels at the lens.

METHODS

A finite-element model for oxygen transport and consumption in the human vitreous was created. Major inputs included ascorbate-mediated oxygen consumption in the vitreous, consumption at the posterior lens surface, and inflow from the retinal vasculature. Concentration-dependent relations were determined from experimental human data or estimated from animal studies, with the impact of all assumptions explored via parameter studies.

RESULTS

The model reproduced experimental data in humans, including oxygen partial pressure (Po2) gradients (≈15 mm Hg) across the anterior-posterior extent of the vitreous body, higher oxygen levels at the pars plana relative to the vitreous core, increases in Po2 near the lens after cataract surgery, and equilibration in the vitreous chamber following vitrectomy. Loss of the antioxidative capacity of ascorbate increases oxygen levels 3-fold at the lens surface. Homogeneous vitreous degeneration (liquefaction), but not partial posterior vitreous detachment, greatly increases oxygen exposure to the lens.

CONCLUSIONS

Ascorbate content and the structure of the vitreous gel are critical determinants of lens oxygen exposure. Minimally invasive surgery and restoration of vitreous structure warrant further attention as strategies for preventing nuclear cataracts.

The challenge of screening for retinopathy of prematurity

Screening for retinopathy of prematurity (ROP) and the optimum treatment of sight-threatening disease requires detailed understanding of the infants at risk and timely identification. Despite a plethora of guidelines, not all populations and situations are adequately covered, so that what should be preventable visual disability still occurs. This article considers the design of screening guidelines and the possibility of a global guideline, although in certain parts of the world manpower for ROP screening is not available. Algorithms linked to the increase in weight of preterm infants over time may refine the number of babies needing to undergo treatment.

Automated separation of binary overlapping trees in low-contrast color retinal images

While many approaches exist for the automated segmentation of retinal vessels in fundus photographs, limited work has focused on the problem of separating the arterial from the venous trees. The few existing approaches that do exist for separating arteries from veins are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to only the very largest vessels. In this work, we propose a new, more global, optimization framework for separating two overlapping trees within medical images and apply this approach for the separation of arteriovenous trees in low-contrast color fundus images. In particular, our approach has two stages. The first stage is to generate a vessel potential connectivity map (VPCM) consisting of vessel segments and the potential connectivity between them. The second stage is to separate the VPCM into multiple anatomical trees using a graph-based meta-heuristic algorithm. Based on a graph model, the algorithm first uses local knowledge and global constraints of the vasculature to generate near-optimal candidate solutions, and then obtains the final solution based on global costs. We test the algorithm on 48 low-contrast fundus images and the promising results suggest its applicability and robustness.

Blood vessel segmentation methodologies in retinal images--a survey

Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.

An automated computational framework for retinal vascular network labeling and branching order analysis

Changes in retinal vascular morphology are well known as predictive clinical signs of many diseases such as hypertension, diabetes and so on. Computer-aid image processing and analysis for retinal vessels in fundus images are effective and efficient in clinical diagnosis instead of tedious manual labeling and measurement. An automated computational framework for retinal vascular network labeling and analysis is presented in this work. The framework includes 1) detecting and locating the optic disc; 2) tracking the vessel centerline from detected seed points and linking the breaks after tracing; 3) extracting all the retinal vascular trees and identifying all the significant points; and 4) classifying terminal points into starting points and ending points based on the information of optic disc location, and finally assigning branch order for each extracted vascular tree in the image. All the modules in the framework are fully automated. Based on the results, morphological analysis is then applied to achieve geometrical and topological features based on branching order for one individual vascular tree or for the vascular network through the retinal vascular network in the images. Validation and experiments on the public DRIVE database have demonstrated that the proposed framework is a novel approach to analyze and study the vascular network pattern, and may offer new insights to the diagnosis of retinopathy.

Three-dimensional reconstruction of blood vessels extracted from retinal fundus images

We present a 3D reconstruction of retinal blood vessel trees using two views of fundus images. The problem is addressed by using well known computer vision techniques which consider: 1) The recovery of camera-eyeball model parameters by an auto-calibration method. The camera parameters are found via the solution of simplified Kruppa equations, based on correspondences found by a LMedS optimisation correlation between pairs of eight different views. 2) The extraction of blood vessels and skeletons from two fundus images. 3) The matching of corresponding points of the two skeleton trees. The trees are previously labelled during the analysis of 2D binary images. Finally, 4) the lineal triangulation of matched correspondence points and the surface modelling via generalised cylinders using diameter measurements extracted from the 2D binary images. The method is nearly automatic and it is tested with 2 sets of 10 fundus retinal images, each one taken from different subjects. Results of 3D vein and artery trees reconstructions are shown.

Fast retinal vessel detection and measurement using wavelets and edge location refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available.