Automatic Identification of Retinal Arteries and Veins From Dual-Wavelength Images Using Structural and Functional Features

Modified histological staining for the identification of arterial and venous segments of brain microvessels

BACKGROUND

This study aimed to develop a modified histochemical staining technique to successfully identify arterial and venous segments of brain microvessels.

NEW METHOD

Gelatin/red ink-alkaline phosphatase-oil red O (GIAO) staining was developed from the traditional gelatin-ink perfusion method. Oil red Chinese ink for brush writing and painting mixed with gelatin was used to label cerebral vascular lumens. Subsequently, alkaline phosphatase staining was used to label endothelial cells on the arterial segments of cerebral microvessels. Thereafter, the red ink color in vessel lumens was highlighted with oil red O staining.

RESULTS

The arterial segments of the brain microvessels exhibited red lumens surrounded by dark blue walls, while the venous segments were bright red following GIAO staining. Meanwhile, the nerve fiber bundles were stained brownish-yellow, and the nuclei appeared light green under light microscope. After cerebral infarction, we used GIAO staining to determine angiogenesis features and detected notable vein proliferation inside the infarct core. Moreover, GIAO staining in conjunction with hematoxylin staining was performed to assess the infiltration of foamy macrophages.

COMPARISON WITH EXISTING METHOD

Red Chinese ink enabled subsequent multiple color staining on brain section. Oil red O was introduced to improved the resolution and contrast between arterial and venous segments of microvessels.

CONCLUSION

With excellent resolution, GIAO staining effectively distinguished arterial and venous segments of microvessels in both normal and ischemic brain tissue. GIAO staining, as described in the present study, will be useful for histological investigations of microvascular bed alterations in a variety of brain disorders.

Computer-aided diagnostic system for hypertensive retinopathy: A review

Hypertensive Retinopathy (HR) is a retinal disease caused by elevated blood pressure for a prolonged period. There are no obvious signs in the early stages of high blood pressure, but it affects various body parts over time, including the eyes. HR is a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks. Early microcirculation abnormalities in chronic diseases can be diagnosed through retinal examination prior to the onset of major clinical consequences. Computer-aided diagnosis (CAD) plays a vital role in the early identification of HR with improved diagnostic accuracy, which is time-efficient and demands fewer resources. Recently, numerous studies have been reported on the automatic identification of HR. This paper provides a comprehensive review of the automated tasks of Artery-Vein (A/V) classification, Arteriovenous ratio (AVR) computation, HR detection (Binary classification), and HR severity grading. The review is conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The paper discusses the clinical features of HR, the availability of datasets, existing methods used for A/V classification, AVR computation, HR detection, and severity grading, and performance evaluation metrics. The reviewed articles are summarized with classifiers details, adoption of different kinds of methodologies, performance comparisons, datasets details, their pros and cons, and computational platform. For each task, a summary and critical in-depth analysis are provided, as well as common research issues and challenges in the existing studies. Finally, the paper proposes future research directions to overcome challenges associated with data set availability, HR detection, and severity grading.

CCS-UNet: a cross-channel spatial attention model for accurate retinal vessel segmentation

Precise segmentation of retinal vessels plays an important role in computer-assisted diagnosis. Deep learning models have been applied to retinal vessel segmentation, but the efficacy is limited by the significant scale variation of vascular structures and the intricate background of retinal images. This paper supposes a cross-channel spatial attention U-Net (CCS-UNet) for accurate retinal vessel segmentation. In comparison to other models based on U-Net, our model employes a ResNeSt block for the encoder-decoder architecture. The block has a multi-branch structure that enables the model to extract more diverse vascular features. It facilitates weight distribution across channels through the incorporation of soft attention, which effectively aggregates contextual information in vascular images. Furthermore, we suppose an attention mechanism within the skip connection. This mechanism serves to enhance feature integration across various layers, thereby mitigating the degradation of effective information. It helps acquire cross-channel information and enhance the localization of regions of interest, ultimately leading to improved recognition of vascular structures. In addition, the feature fusion module (FFM) module is used to provide semantic information for a more refined vascular segmentation map. We evaluated CCS-UNet based on five benchmark retinal image datasets, DRIVE, CHASEDB1, STARE, IOSTAR and HRF. Our proposed method exhibits superior segmentation efficacy compared to other state-of-the-art techniques with a global accuracy of 0.9617/0.9806/0.9766/0.9786/0.9834 and AUC of 0.9863/0.9894/0.9938/0.9902/0.9855 on DRIVE, CHASEDB1, STARE, IOSTAR and HRF respectively. Ablation studies are also performed to evaluate the the relative contributions of different architectural components. Our proposed model is potential for diagnostic aid of retinal diseases.

Full-field optical multi-functional angiography based on endogenous hemodynamic characteristics

Blood flow functional imaging is widely applied in biological research to provide vascular morphological and statistical parameters. It relies on the absorption difference and is, therefore, easily affected by complex biological structures, and it cannot accommodate abundant functional information. We propose a full-field multi-functional angiography method to classify arteriovenous vessels and to display flow velocity and vascular diameter distribution simultaneously. Unlike previous methods, an under-sampled laser Doppler acquisition mode is used to record the low-coherence speckle, and multi-functional angiography is achieved by modulating the endogenous hemodynamic characteristics from low-coherence speckle. To demonstrate the combination of classified angiography, blood flow velocity measurement, and vascular diameter measurement realized using our method, we performed experiments on the flow phantom and living chicken embryos and generated multi-functional angiograms. The proposed method can be used as a label-free multi-functional angiography technique in which red blood cells provide a strong endogenous source of naturally hemodynamic characteristics.

Differential artery-vein analysis in quantitative retinal imaging: a review

Quantitative retinal imaging is essential for eye disease detection, staging classification, and treatment assessment. It is known that different eye diseases or severity stages can affect the artery and vein systems in different ways. Therefore, differential artery-vein (AV) analysis can improve the performance of quantitative retinal imaging. In this article, we provide a brief summary of technical rationales and clinical applications of differential AV analysis in fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA).

In vivo identification of arteries and veins using two-photon excitation elastin autofluorescence

Distinguishing arteries from veins in vivo has a great significance in clinical practices and preclinical studies. Optical imaging methods such as two-photon microscopy can provide high-resolution morphological information of tissue and are therefore extremely suitable for imaging small blood vessels. However, few optical imaging methods allow in vivo identification of arteries and veins merely utilizing the autofluorescence signal of blood vessels. In this report, we found the arterial wall generates a remarkably stronger two-photon excitation autofluorescence (TPEA) signal compared with the venous wall based on BALB/c mice. According to histological analysis and fluorescence characteristic measurement, the contrast signal is confirmed to be from elastin fibers. Employing this unique feature, we propose an objective and effective artery-vein separation strategy that considers the presence of the elastin-TPEA border as the indicator of arteries. Using this strategy, the arterial and venous networks of the dorsal skin and cerebral cortex of BALB/c mice are demonstrated to be excellently mapped and accurately separated in vivo without depending on any exogenous contrast agent, empirical knowledge, and algorithm. This study may provide a novel technique for mapping arterial and venous networks for anatomic research as well as an extra aid to basic researches on the mechanism, diagnosis, and treatment of blood vessel-related diseases.

Optical Access to Arteriovenous Cerebral Microcirculation Through a Transparent Cranial Implant

BACKGROUND AND OBJECTIVE

Microcirculation plays a critical role in physiologic processes and several disease states. Laser speckle imaging (LSI) is a full-field, real-time imaging technique capable of mapping microvessel networks and providing relative flow velocity within the vessels. In this study, we demonstrate that LSI combine with multispectral reflectance imaging (MSRI), which allows for distinction between veins and arteries in the vascular flow maps produced by LSI. We apply this combined technique to mouse cerebral vascular network in vivo, comparing imaging through the skull, to the dura mater and brain directly through a craniectomy, and through a transparent cranial "Window to the Brain" (WttB) implant.

STUDY DESIGN/MATERIALS AND METHODS

The WttB implant used in this study is made of a nanocrystalline Yttria-Stabilized-Zirconia ceramic. MSRI was conducted using white-light illumination and filtering the reflected light for 560, 570, 580, 590, 600, and 610 nm. LSI was conducted using an 810 nm continuous wave near-infrared laser with incident power of 100 mW, and the reflected speckle pattern was captured by a complementary metal-oxide-semiconductor (CMOS) camera.

RESULTS

Seven vessel branches were analyzed and comparison was made between imaging through the skull, craniectomy, and WttB implant. Through the skull, MSRI did not detect any vessels, and LSI could not image microvessels. Imaging through the WttB implant, MSRI was able to identify veins versus arteries, and LSI was able to image microvessels with only slightly higher signal-to-noise ratio and lower sharpness than imaging the brain through a craniectomy.

CONCLUSIONS

This study demonstrates the ability to perform MSRI-LSI across a transparent cranial implant, to allow for cerebral vascular networks to be mapped, including microvessels. These images contain additional information such as vein-artery separation and relative blood flow velocities, information which is of value scientifically and medically. The WttB implant provides substantial improvements over imaging through the murine cranial bone, where microvessels are not visible and MSRI cannot be performed. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.

Color Fundus Image Guided Artery-Vein Differentiation in Optical Coherence Tomography Angiography

Purpose

This study aimed to develop a method for automated artery-vein classification in optical coherence tomography angiography (OCTA), and to verify that differential artery-vein analysis can improve the sensitivity of OCTA detection and staging of diabetic retinopathy (DR).

Methods

For each patient, the color fundus image was used to guide the artery-vein differentiation in the OCTA image. Traditional mean blood vessel caliber (m-BVC) and mean blood vessel tortuosity (m-BVT) in OCTA images were quantified for control and DR groups. Artery BVC (a-BVC), vein BVC (v-BVC), artery BVT (a-BVT), and vein BVT (a-BVT) were calculated, and then the artery-vein ratio (AVR) of BVC (AVR-BVC) and AVR of BVT (AVR-BVT) were quantified for comparative analysis. Sensitivity, specificity, and accuracy were used as performance metrics of artery-vein classification. One-way, multilabel ANOVA with Bonferroni's test and Student's t-test were employed for statistical analysis.

Results

Forty eyes of 20 control subjects and 80 eyes of 48 NPDR patients (18 mild, 16 moderate, and 14 severe NPDR) were evaluated in this study. The color fundus image-guided artery-vein differentiation reliably identified individual arteries and veins in OCTA. AVR-BVC and AVR-BVT provided significant (P < 0.001) and moderate (P < 0.05) improvements, respectively, in detecting and classifying NPDR stages, compared with traditional m-BVC analysis.

Conclusions

Color fundus image-guided artery-vein classification provides a feasible method to differentiate arteries and veins in OCTA. Differential artery-vein analysis can improve the sensitivity of OCTA detection and classification of DR. AVR-BVC is the most-sensitive feature, which can classify control and mild NPDR, providing a quantitative biomarker for objective detection of early DR.

A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation

Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All existing deep learning models attempt to segment both types of vessels simultaneously by using a unified pixel-wise loss that treats all vessel pixels with equal importance. Due to the highly imbalanced ratio between thick vessels and thin vessels (namely the majority of vessel pixels belong to thick vessels), the pixel-wise loss would be dominantly guided by thick vessels and relatively little influence comes from thin vessels, often leading to low segmentation accuracy for thin vessels. To address the imbalance problem, in this paper, we explore to segment thick vessels and thin vessels separately by proposing a three-stage deep learning model. The vessel segmentation task is divided into three stages, namely thick vessel segmentation, thin vessel segmentation, and vessel fusion. As better discriminative features could be learned for separate segmentation of thick vessels and thin vessels, this process minimizes the negative influence caused by their highly imbalanced ratio. The final vessel fusion stage refines the results by further identifying nonvessel pixels and improving the overall vessel thickness consistency. The experiments on public datasets DRIVE, STARE, and CHASE_DB1 clearly demonstrate that the proposed three-stage deep learning model outperforms the current state-of-the-art vessel segmentation methods.

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.

Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database

The segmentation and classification of retinal arterioles and venules play an important role in the diagnosis of various eye diseases and systemic diseases. The major challenges include complicated vessel structure, inhomogeneous illumination, and large background variation across subjects. In this study, we employ a fully convolutional network to simultaneously segment arterioles and venules directly from the retinal image, rather than using a vessel segmentation-arteriovenous classification strategy as reported in most literature. To simultaneously segment retinal arterioles and venules, we configured the fully convolutional network to allow true color image as input and multiple labels as output. A domain-specific loss function was designed to improve the overall performance. The proposed method was assessed extensively on public data sets and compared with the state-of-the-art methods in literature. The sensitivity and specificity of overall vessel segmentation on DRIVE is 0.944 and 0.955 with a misclassification rate of 10.3% and 9.6% for arteriole and venule, respectively. The proposed method outperformed the state-of-the-art methods and avoided possible error-propagation as in the segmentation-classification strategy. The proposed method was further validated on a new database consisting of retinal images of different qualities and diseases. The proposed method holds great potential for the diagnostics and screening of various eye diseases and systemic diseases.

Automated classification and quantitative analysis of arterial and venous vessels in fundus images

It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).

Contact-free trans-pars-planar illumination enables snapshot fundus camera for nonmydriatic wide field photography

In conventional fundus photography, trans-pupillary illumination delivers illuminating light to the interior of the eye through the peripheral area of the pupil, and only the central part of the pupil can be used for collecting imaging light. Therefore, the field of view of conventional fundus cameras is limited, and pupil dilation is required for evaluating the retinal periphery which is frequently affected by diabetic retinopathy (DR), retinopathy of prematurity (ROP), and other chorioretinal conditions. We report here a nonmydriatic wide field fundus camera employing trans-pars-planar illumination which delivers illuminating light through the pars plana, an area outside of the pupil. Trans-pars-planar illumination frees the entire pupil for imaging purpose only, and thus wide field fundus photography can be readily achieved with less pupil dilation. For proof-of-concept testing, using all off-the-shelf components a prototype instrument that can achieve 90° fundus view coverage in single-shot fundus images, without the need of pharmacologic pupil dilation was demonstrated.

Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation

OBJECTIVE

Deep learning based methods for retinal vessel segmentation are usually trained based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to-pixel matching between a predicted probability map and the corresponding manually annotated segmentation. However, due to the highly imbalanced pixel ratio between thick and thin vessels in fundus images, a pixel-wise loss would limit deep learning models to learn features for accurate segmentation of thin vessels, which is an important task for clinical diagnosis of eye-related diseases.

METHODS

In this paper, we propose a new segment-level loss which emphasizes more on the thickness consistency of thin vessels in the training process. By jointly adopting both the segment-level and the pixel-wise losses, the importance between thick and thin vessels in the loss calculation would be more balanced. As a result, more effective features can be learned for vessel segmentation without increasing the overall model complexity.

RESULTS

Experimental results on public data sets demonstrate that the model trained by the joint losses outperforms the current state-of-the-art methods in both separate-training and cross-training evaluations.

CONCLUSION

Compared to the pixel-wise loss, utilizing the proposed joint-loss framework is able to learn more distinguishable features for vessel segmentation. In addition, the segment-level loss can bring consistent performance improvement for both deep and shallow network architectures.

SIGNIFICANCE

The findings from this study of using joint losses can be applied to other deep learning models for performance improvement without significantly changing the network architectures.

Combining ODR and Blood Vessel Tracking for Artery-Vein Classification and Analysis in Color Fundus Images

Purpose

This study aims to develop a fully automated algorithm for artery–vein (A-V) and arteriole-venule classification and to quantify the effect of hypertension on A-V caliber and tortuosity ratios of nonproliferative diabetic retinopathy (NPDR) patients.

Methods

We combine an optical density ratio (ODR) analysis and blood vessel tracking (BVT) algorithm to classify arteries and veins and arterioles and venules. An enhanced blood vessel map and ODR analysis are used to determine the blood vessel source nodes. The whole vessel map is then tracked beginning from the source nodes and classified as vein (venule) or artery (arteriole) using vessel curvature and angle information. Fifty color fundus images from NPDR patients are used to test the algorithm. Sensitivity, specificity, and accuracy metrics are measured to validate the classification method compared to ground truths.

Results

The combined ODR-BVT method demonstrates 97.06% accuracy in identifying blood vessels as vein or artery. Sensitivity and specificity of A-V identification are 97.58%, 97.81%, and 95.89%, 96.68%, respectively. Comparative analysis revealed that the average A-V caliber and tortuosity ratios of NPDR patients with hypertension have 48% and 15.5% decreases, respectively, compared to that of NPDR patients without hypertension.

Conclusions

Automated A-V classification has been achieved by combined ODR-BVT analysis. Quantitative analysis of color fundus images verified robust performance of the A-V classification. Comparative quantification of A-V caliber and tortuosity ratios provided objective biomarkers to differentiate NPDR groups with and without hypertension.

Translational Relevance

Automated A-V classification can facilitate quantitative analysis of retinal vascular distortions due to diabetic retinopathy and other eye conditions and provide increased sensitivity for early detection of eye diseases.

A Skeletal Similarity Metric for Quality Evaluation of Retinal Vessel Segmentation

The most commonly used evaluation metrics for quality assessment of retinal vessel segmentation are sensitivity, specificity, and accuracy, which are based on pixel-to-pixel matching. However, due to the inter-observer problem that vessels annotated by different observers vary in both thickness and location, pixel-to-pixel matching is too restrictive to fairly evaluate the results of vessel segmentation. In this paper, the proposed skeletal similarity metric is constructed by comparing the skeleton maps generated from the reference and the source vessel segmentation maps. To address the inter-observer problem, instead of using a pixel-to-pixel matching strategy, each skeleton segment in the reference skeleton map is adaptively assigned with a searching range whose radius is determined based on its vessel thickness. Pixels in the source skeleton map located within the searching range are then selected for similarity calculation. The skeletal similarity consists of a curve similarity, which measures the structural similarity between the reference and the source skeleton maps and a thickness similarity, which measures the thickness consistency between the reference and the source vessel segmentation maps. In contrast to other metrics that provide a global score for the overall performance, we modify the definitions of true positive, false negative, true negative, and false positive based on the skeletal similarity, based on which sensitivity, specificity, accuracy, and other objective measurements can be constructed. More importantly, the skeletal similarity metric has better potential to be used as a pixelwise loss function for training deep learning models for retinal vessel segmentation. Through comparison of a set of examples, we demonstrate that the redefined metrics based on the skeletal similarity are more effective for quality evaluation, especially with greater tolerance to the inter-observer problem.

Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy

BACKGROUND AND OBJECTIVES

Hypertensive Retinopathy (HR) is a retinal disease which happened due to consistent high blood pressure (hypertension). In this paper, an automated system is presented that detects the HR at various stages using arteriovenous ratio and papilledema signs through fundus retinal images.

METHODS

The proposed system consists of two modules i.e. vascular analysis for calculation of arteriovenous ratio and optic nerve head (ONH) region analysis for papilledema.  First module uses a set of hybrid features in Artery or Vein (A/V) classification using support vector machine (SVM) along with its radial basis function (RBF) kernel for arteriovenous ratio. In second module, proposed system performs analysis of ONH region for possible signs of papilledema. This stage utilizes different features along with SVM and RBF for classification of papilledema.

RESULTS

The first module of proposed method shows average accuracies of 95.10%, 95.64% and 98.09%for images of INSPIRE-AVR, VICAVR, and local dataset respectively. The second module of proposed method achieves average accuracies of 95.93% and 97.50% on STARE and local dataset respectively.

CONCLUSIONS

The system finally utilizes results from both modules to grade HR with good results. The presented system is a novel step towards automated detection and grading of HR disease and can be used as clinical decision support system.

Retinal Blood Vessel Segmentation Using Extreme Learning Machine

Extreme learning machine (ELM) is an effective machine learning technique that widely used in image processing. In this paper, a new supervised method for segmenting blood vessels in retinal images is proposed based on the ELM classifier. The proposed algorithm first constructs a 7-D feature vector using multi-scale Gabor filter, Hessian matrix and bottom-hat transformation. Then, an ELM classifier is trained on gold standard examples of vessel segmentation images to classify previous unseen images. The algorithm was tested on the publicly available DRIVE database – a digital image database for vessel extraction. Experimental results on both real-captured images and public database images demonstrate that our method shows comparative performance against other methods, which make the proposed algorithm a suitable tool for automated retinal image analysis.

Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

Extraction of blood vessels in retinal images is an important step for computer-aided diagnosis of ophthalmic pathologies. We propose an approach for blood vessel tracking and diameter estimation. We hypothesize that the curvature and the diameter of blood vessels are Gaussian processes (GPs). Local Radon transform, which is robust against noise, is subsequently used to compute the features and train the GPs. By learning the kernelized covariance matrix from training data, vessel direction and its diameter are estimated. In order to detect bifurcations, multiple GPs are used and the difference between their corresponding predicted directions is quantified. The combination of Radon features and GP results in a good performance in the presence of noise. The proposed method successfully deals with typically difficult cases such as bifurcations and central arterial reflex, and also tracks thin vessels with high accuracy. Experiments are conducted on the publicly available DRIVE, STARE, CHASEDB1, and high-resolution fundus databases evaluating sensitivity, specificity, and Matthew's correlation coefficient (MCC). Experimental results on these datasets show that the proposed method reaches an average sensitivity of 75.67%, specificity of 97.46%, and MCC of 72.18% which is comparable to the state-of-the-art.

Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE

It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate.

Accessing to arteriovenous blood flow dynamics response using combined laser speckle contrast imaging and skin optical clearing

Laser speckle contrast imaging (LSCI) shows a great potential for monitoring blood flow, but the spatial resolution suffers from the scattering of tissue. Here, we demonstrate the capability of a combination method of LSCI and skin optical clearing to describe in detail the dynamic response of cutaneous vasculature to vasoactive noradrenaline injection. Moreover, the superior resolution, contrast and sensitivity make it possible to rebuild arteries-veins separation and quantitatively assess the blood flow dynamical changes in terms of flow velocity and vascular diameter at single artery or vein level.

Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study

BACKGROUND

Detection of retinal lesions like micro-aneurysms and exudates are important for the clinical diagnosis of diabetes retinopathy. The traditional subjective judgments by clinicians are dependent on their experience and can be subject to lack of consistency and therefore a quantification method is worthwhile.

METHODS

In this study, 10 moderate non-proliferative diabetes retinopathy (NPDR) patients and 10 severe NPDR ones were retrospectively selected as a cohort. Mathematical morphological methods were used for automatic segmentation of lesions. For exudates detection, images were pre-processed with adaptive histogram equalization to enhance contrast, then binary images for area calculation were obtained by threshold classification. For micro-aneurysms detection, the images were pre-processed by top-hat and bottom-hat transformation, then Otsu method and Hough transform were used to classify micro-aneurysms. Post-processing morphological methods were used to preclude the false positive noise.

RESULTS

After segmentation, the area of exuduates divided by optic disk area (exudates/disk ratio) and counts of microaneurysms were quantified and compared between the moderate and severe non-proliferative diabetic retinopathy groups, which had significant difference(P < 0.05).

CONCLUSIONS

In conclusion, morphological features of lesion might be an image marker for NPDR grading and computer aided quantification of retinal lesion could be a practical way for clinicians to better investigates diabetic retinopathy.

Vessel labeling in combined confocal scanning laser ophthalmoscopy and optical coherence tomography images: criteria for blood vessel discrimination

Introduction

The diagnostic potential of optical coherence tomography (OCT) in neurological diseases is intensively discussed. Besides the sectional view of the retina, modern OCT scanners produce a simultaneous top-view confocal scanning laser ophthalmoscopy (cSLO) image including the option to evaluate retinal vessels. A correct discrimination between arteries and veins (labeling) is vital for detecting vascular differences between healthy subjects and patients. Up to now, criteria for labeling (cSLO) images generated by OCT scanners do not exist.

Objective

This study reviewed labeling criteria originally developed for color fundus photography (CFP) images.

Methods

The criteria were modified to reflect the cSLO technique, followed by development of a protocol for labeling blood vessels. These criteria were based on main aspects such as central light reflex, brightness, and vessel thickness, as well as on some additional criteria such as vascular crossing patterns and the context of the vessel tree.

Results and Conclusion

They demonstrated excellent inter-rater agreement and validity, which seems to indicate that labeling of images might no longer require more than one rater. This algorithm extends the diagnostic possibilities offered by OCT investigations.

Pathway to Retinal Oximetry

Events and discoveries in oxygen monitoring over the past two centuries are presented as the background from which oximetry of the human retina evolved. Achievements and the people behind them are discussed, showing parallels between the work in tissue measurements and later in the eye. Developments in the two-wavelength technique for oxygen saturation measurements in retinal vessels are shown to exploit the forms of imaging technology available over time. The last section provides a short summary of the recent research in retinal diseases using vessel oximetry.

Normative values and predictors of retinal oxygen saturation

PURPOSE

To determine normal retinal oxygen saturation (SO2) values measured with retinal oximetry in a multiethnic group of healthy subjects and to evaluate the association of retinal SO2 with demographic and clinical parameters.

METHODS

Retinal oximetry was performed in both eyes of 61 normal healthy subjects. Global and quadrant venous (SvO2) and arterial oxygen saturation (SaO2), arteriovenous difference in SO2, and venular and arteriolar width were measured. The association of SO2 parameters with age, gender, ethnicity, refraction, iris color, history of controlled systemic hypertension, and smoking was analyzed.

RESULTS

Average SvO2 and SaO2 were 55.3 ± 7.1% and 90.4 ± 4.3%, respectively. All average measurements were comparable in both eyes, both genders, and among ethnic groups. Inferonasal quadrant SaO2 was higher in Asians. Age was associated with decreased SvO2 (β = -0.19; P = 0.001) and SaO2 (β = -0.11; P = 0.003). History of controlled systemic hypertension was associated with an increase in arteriovenous difference in SO2 (β = 3.99; P = 0.013).

CONCLUSION

This is the first description of retinal SO2 in healthy, multiethnic subjects. Aging decreases SvO2 and SaO2 and should be accounted for when interpreting retinal oximetry measurements. Other demographic and clinical parameters studied did not seem to significantly influence retinal SO2 measurements.

Retinal vessel classification: sorting arteries and veins

For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an Expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in [12] [14] and [15]. The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in [13]. The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.

An approach to localize the retinal blood vessels using bit planes and centerline detection

The change in morphology, diameter, branching pattern or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.

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.

NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer's disease

This paper presents a novel computer-aided diagnosis (CAD) technique for the early diagnosis of the Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds of confidence. The CAD tool is designed for the study and classification of functional brain images. For this purpose, two different brain image databases are selected: a single photon emission computed tomography (SPECT) database and positron emission tomography (PET) images, both of them containing data for both Alzheimer's disease (AD) patients and healthy controls as a reference. These databases are analyzed by applying the Fisher discriminant ratio (FDR) and nonnegative matrix factorization (NMF) for feature selection and extraction of the most relevant features. The resulting NMF-transformed sets of data, which contain a reduced number of features, are classified by means of a SVM-based classifier with bounds of confidence for decision. The proposed NMF-SVM method yields up to 91% classification accuracy with high sensitivity and specificity rates (upper than 90%). This NMF-SVM CAD tool becomes an accurate method for SPECT and PET AD image classification.

Cerebral artery-vein separation using 0.1-Hz oscillation in dual-wavelength optical imaging

We present a novel artery-vein separation method using 0.1-Hz oscillation at two wavelengths with optical imaging of intrinsic signals (OIS). The 0.1-Hz oscillation at a green light wavelength of 546 nm exhibits greater amplitude in arteries than in veins and is primarily caused by vasomotion, whereas the 0.1-Hz oscillation at a red light wavelength of 630 nm exhibits greater amplitude in veins than in arteries and is primarily caused by changes of deoxyhemoglobin concentration. This spectral feature enables cortical arteries and veins to be segmented independently. The arteries can be segmented on the 0.1-Hz amplitude image at 546 nm using matched filters of a modified dual Gaussian model combining with a single Gaussian model. The veins are a combination of vessels segmented on both amplitude images at the two wavelengths using multiscale matched filters of single Gaussian model. Our method can separate most of the thin arteries and veins from each other, especially the thin arteries with low contrast in raw gray images. In vivo OIS experiments demonstrate the separation ability of the 0.1-Hz based segmentation method in cerebral cortex of eight rats. Two validation studies were undertaken to evaluate the performance of the method by quantifying the arterial and venous length based on a reference standard. The results indicate that our 0.1-Hz method is very effective in separating both large and thin arteries and veins regardless of vessel crossover or overlapping to great extent in comparison with previous methods.

Simultaneous automatic arteries-veins separation and cerebral blood flow imaging with single-wavelength laser speckle imaging

Automatic separation of arteries and veins in optical cerebral cortex images is important in clinical practice and preclinical study. In this paper, a simple but effective automatic artery-vein separation method which utilizes single-wavelength coherent illumination is presented. This method is based on the relative temporal minimum reflectance analysis of laser speckle images. The validation is demonstrated with both theoretic simulations and experimental results applied to the rat cortex. Moreover, this method can be combined with laser speckle contrast analysis so that the artery-vein separation and blood flow imaging can be simultaneously obtained using the same raw laser speckle images data to enable more accurate analysis of changes of cerebral blood flow within different tissue compartments during functional activation, disease dynamic, and neurosurgery, which may broaden the applications of laser speckle imaging in biology and medicine.

Separation of arteries and veins in the cerebral cortex using physiological oscillations by optical imaging of intrinsic signal

An automated method is presented for artery-vein separation in cerebral cortical images recorded with optical imaging of the intrinsic signal. The vessel-type separation method is based on the fact that the spectral distribution of intrinsic physiological oscillations varies from arterial regions to venous regions. In arterial regions, the spectral power is higher in the heartbeat frequency (HF), whereas in venous regions, the spectral power is higher in the respiration frequency (RF). The separation method was begun by extracting the vascular network and its centerline. Then the spectra of the optical intrinsic signals were estimated by the multitaper method. A standard F-test was performed on each discrete frequency point to test the statistical significance at the given level. Four periodic physiological oscillations were examined: HF, RF, and two other eigenfrequencies termed F1 and F2. The separation of arteries and veins was implemented with the fuzzy c-means clustering method and the region-growing approach by utilizing the spectral amplitudes and power-ratio values of the four eigenfrequencies on the vasculature. Subsequently, independent spectral distributions in the arteries, veins, and capillary bed were estimated for comparison, which showed that the spectral distributions of the intrinsic signals were very distinct between the arterial and venous regions.