The detection and quantification of retinopathy using digital angiograms

Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks

The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44 correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42.

Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation

The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710.

Application of morphological bit planes in retinal blood vessel extraction

The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.

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

We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface. We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy screening programme and annotated manually by two clinicians. We obtain considerably better accuracies compared to leading methods in REVIEW tests and in Tayside tests. An important advantage of our method is its stability (success rate, i.e., meaningful measurement returned, of 100% on all REVIEW data sets and on the Tayside data set) compared to a variety of methods from the literature. We also find that results depend crucially on testing data and conditions, and discuss criteria for selecting a training set yielding optimal accuracy.

Retinal artery-vein caliber grading using color fundus imaging

Recent research suggests that retinal vessel caliber (or cross-sectional width) measured from retinal photographs is an important feature for predicting cardiovascular diseases (CVDs). One of the most utilized measures is to quantify retinal arteriolar and venular caliber as the Central Retinal Artery Equivalent (CRAE) and Central Retinal Vein Equivalent (CRVE). However, current computer tools utilize manual or semi-automatic grading methods to estimate CRAE and CRVE. These methods involve a significant amount of grader's time and can add a significant level of inaccuracy due to repetitive nature of grading and intragrader distances. An automatic and time efficient grading of the vessel caliber with highly repeatable measurement is essential, but is technically challenging due to a substantial variation of the retinal blood vessels' properties. In this paper, we propose a new technique to measure the retinal vessel caliber, which is an "edge-based" vessel tracking method. We measured CRAE and CRVE from each of the vessel types. We achieve very high accuracy (average 96.23%) for each of the cross-sectional width measurement compared to manually graded width. For overall vessel caliber measurement accuracy of CRAE and CRVE, we compared the results with an existing semi-automatic method which showed high correlation of 0.85 and 0.92, respectively. The intra-grader reproducibility of our method was high, with the correlation coefficient of 0.881 for CRAE and 0.875 for CRVE.

An accurate multimodal 3-D vessel segmentation method based on brightness variations on OCT layers and curvelet domain fundus image analysis

This paper proposes a multimodal approach for vessel segmentation of macular optical coherence tomography (OCT) slices along with the fundus image. The method is comprised of two separate stages; the first step is 2-D segmentation of blood vessels in curvelet domain, enhanced by taking advantage of vessel information in crossing OCT slices (named feedback procedure), and improved by suppressing the false positives around the optic nerve head. The proposed method for vessel localization of OCT slices is also enhanced utilizing the fact that retinal nerve fiber layer becomes thicker in the presence of the blood vessels. The second stage of this method is axial localization of the vessels in OCT slices and 3-D reconstruction of the blood vessels. Twenty-four macular spectral 3-D OCT scans of 16 normal subjects were acquired using a Heidelberg HRA OCT scanner. Each dataset consisted of a scanning laser ophthalmoscopy (SLO) image and limited number of OCT scans with size of 496 × 512 (namely, for a data with 19 selected OCT slices, the whole data size was 496 × 512 × 19). The method is developed with least complicated algorithms and the results show considerable improvement in accuracy of vessel segmentation over similar methods to produce a local accuracy of 0.9632 in area of SLO, covered with OCT slices, and the overall accuracy of 0.9467 in the whole SLO image. The results are also demonstrative of a direct relation between the overall accuracy and percentage of SLO coverage by OCT slices.

Measurement of retinal arteriolar diameters from auto scale phase congruency with fuzzy weighting and L1 regularization

Manual measurements of small changes in retinal vascular diameter are slow and may be subject to considerable observer-related biases. Among the conventional automatic methods the sliding linear regression filter (SLRF) demonstrates the least scattered and most repeatable coefficients. For optimal performance it relies on the choice of the correct filter scale for different vessel sizes. A small scale extracts fine details at the expense noise sensitivity, while large scales have poor edge localization. Here we use auto scale phase congruency to select the filter scales with fuzzy weighting to reduce noise, and L1 regularization for edge smoothing. Our method uses a one dimensional analysis normal to the vessel and so is faster than the 2D phase congruency. In 65 vessels randomly selected from 20 images the proposed method showed better repeatability and over three times less scattering than conventional SLRF.

Retinal vessel diameter measurement using unsupervised linear discriminant analysis

An automatic vessel diameter measurement technique based on linear discriminant analysis (LDA) has been proposed. After estimating the vessel wall, the vessel cross-section profile is divided into three regions: two corresponding to the background and one to the vessel. The algorithm was tested on more than 5000 cross-sections of retinal vessels from the REVIEW dataset through comparative study with the state-of-the-art techniques. Cross-correlation analyses were performed to determine the degree to which the proposed technique was close to the ground truth. The results indicate that proposed algorithm consistently performed better than most of other techniques and was highly correlated with the manual measurement as the reference diameter. The proposed method does not require any supervision and is suitable for automatic analysis.

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.

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.

A function for quality evaluation of retinal vessel segmentations

Retinal blood vessel assessment plays an important role in the diagnosis of ophthalmic pathologies. The use of digital images for this purpose enables the application of a computerized approach and has fostered the development of multiple methods for automated vascular tree segmentation. Metrics based on contingency tables for binary classification have been widely used for evaluating the performance of these algorithms. Metrics from this family are based on the measurement of a success or failure rate in the detected pixels, obtained by means of pixel-to-pixel comparison between the automated segmentation and a manually-labeled reference image. Therefore, vessel pixels are not considered as a part of a vascular structure with specific features. This paper contributes a function for the evaluation of global quality in retinal vessel segmentations. This function is based on the characterization of vascular structures as connected segments with measurable area and length. Thus, its design is meant to be sensitive to anatomical vascularity features. Comparison of results between the proposed function and other general quality evaluation functions shows that this proposal renders a high matching degree with human quality perception. Therefore, it can be used to enhance quality evaluation in retinal vessel segmentations, supplementing the existing functions. On the other hand, from a general point of view, the applied concept of measuring descriptive properties may be used to design specialized functions aimed at segmentation quality evaluation in other complex structures.

MORPHOLOGICAL STRUCTURE RECONSTRUCTION OF RETINAL VESSELS IN FUNDUS IMAGES

Vessels in retinal fundus images are useful in revealing the severity of eye-related diseases. In addition, they can act as landmarks for localizing lesions or the central vision area, and guide laser treatment of neovascularization. In this paper, we propose a two-stage scheme to extract vessels and reconstruct the morphological structure of vessels in retinal images. First, we employ mathematical morphology techniques to highlight large and small vessels with respect to their spatial properties. Different curvature response between vessel and noise patterns allows the use of curvature evaluation to remove enhanced vessel-like noise. A set of linear filters finalize the vessel map. However, the resulting vascular structure is incomplete of some important features in bifurcation points and central reflex. In order to rectify the pitfall, a reconstruction process is performed using dynamic local region growth to recover the morphological structure of vessels. Average performance of our method to extract vessels is 83.7% of TPR(True positive rate) and 3.8% of FPR(False positive rate) for 35 retinal images which include 21 abnormal images.

Automated localization of retinal features

Retinal fundus images are widely used in the diagnosis and treatment of various eye diseases, such as diabetic retinopathy and glaucoma. A computer-aided retinal fundus image analysis could provide an immediate detection and characterization of retinal features prior to specialist inspection. This paper proposes an approach to automatically localize the main features in fundus images, such as blood vessels, optic disc, and fovea by exploiting the spatial and geometric relations that govern their distribution within the fundus image. The blood vessels are segmented by scale-space analysis. The average thickness of these blood vessels is then computed using the vessels centerlines and orientations from a Hessian matrix. The optic disc is localized using the circular Hough transform, the parabolic Hough transform fitting, and the localization of the fovea. The proposed method can be extended to establish a foveal coordinate system to facilitate grading lesions based on the spatial relationships between lesions and landmark features. The proposed method was evaluated on publicly available image databases, and the results have demonstrated a significant improvement over the current state-of-the-art methods.

Vessel boundary delineation on fundus images using graph-based approach

This paper proposes an algorithm to measure the width of retinal vessels in fundus photographs using graph-based algorithm to segment both vessel edges simultaneously. First, the simultaneous two-boundary segmentation problem is modeled as a two-slice, 3-D surface segmentation problem, which is further converted into the problem of computing a minimum closed set in a node-weighted graph. An initial segmentation is generated from a vessel probability image. We use the REVIEW database to evaluate diameter measurement performance. The algorithm is robust and estimates the vessel width with subpixel accuracy. The method is used to explore the relationship between the average vessel width and the distance from the optic disc in 600 subjects.

Retinal vessel extraction by combining radial symmetry transform and iterated graph cuts

In this paper, we propose a new method for the extraction of blood vessels in retinal images. This approach starts with a Hessian-based multiscale filtering method to enhance blood vessels in gray retinal images. Subsequently, a new radial symmetry transformation, which is based on line kernels, is proposed to improve the detection of vessel structures and restrain the response of nonvessel structures. Finally, an iterated segmentation algorithm is used to extract retinal vessels. The proposed approach has been tested on the two publicly available databases, DRIVE and STARE. The experimental results show the feasibility of the proposed method.

A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features

This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

Salient feature region: a new method for retinal image registration

Retinal image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality retinal images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new retinal image registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for retinal image registration.

Automatic model-based tracing algorithm for vessel segmentation and diameter estimation

An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.

A partial intensity invariant feature descriptor for multimodal retinal image registration

Detection of vascular bifurcations is a challenging task in multimodal retinal image registration. Existing algorithms based on bifurcations usually fail in correctly aligning poor quality retinal image pairs. To solve this problem, we propose a novel highly distinctive local feature descriptor named partial intensity invariant feature descriptor (PIIFD) and describe a robust automatic retinal image registration framework named Harris-PIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our Harris-PIIFD framework consists of four steps. First, corner points are used as control point candidates instead of bifurcations since corner points are sufficient and uniformly distributed across the image domain. Second, PIIFDs are extracted for all corner points, and a bilateral matching technique is applied to identify corresponding PIIFDs matches between image pairs. Third, incorrect matches are removed and inaccurate matches are refined. Finally, an adaptive transformation is used to register the image pairs. PIIFD is so distinctive that it can be correctly identified even in nonvascular areas. When tested on 168 pairs of multimodal retinal images, the Harris-PIIFD far outperforms existing algorithms in terms of robustness, accuracy, and computational efficiency.

An active contour model for segmenting and measuring retinal vessels

This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a "Ribbon of Twins" active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with subpixel average width errors. We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement.

Unsupervised fuzzy based vessel segmentation in pathological digital fundus images

Performing the segmentation of vasculature in the retinal images having pathology is a challenging problem. This paper presents a novel approach for automated segmentation of the vasculature in retinal images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of blood vessels against the background. The enhanced blood vessels are then segmented by employing spatially weighted fuzzy c-means clustering based thresholding which can well maintain the spatial structure of the vascular tree segments. The proposed method's performance is evaluated on publicly available DRIVE and STARE databases of manually labeled images. On the DRIVE and STARE databases, it achieves an area under the receiver operating characteristic curve of 0.9518 and 0.9602 respectively, being superior to those presented by state-of-the-art unsupervised approaches and comparable to those obtained with the supervised methods.

Multiscale active contour model for vessel segmentation

This paper presents a novel multiscale active contour model for vessel segmentation. The model is based on accurate analysis of the vessel structure in the image. According to different scale response of the eigenvalues of local second order derivative (Hessian matrix), a new vessel region information function, which shows a valid estimation of the vesselness measure, is defined. We introduce the posteriori probability estimation into the active contours framework and design a new objective function. The defined objective function is minimized using the variational method, and a new region-based external force is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. This active contour model combines the obtained region-based and conventional boundary-based force, which aims at finding more accurate vessel edges even when the vessel branches are low contrast or blurry. Furthermore, the proposed model is implemented by an implicit method of level set framework, the solution of which is steady and suitable for various topology changes. Moreover, two new speed functions for vessel segmentation in the level set method are presented, one for fast marching and the other for a narrow-band algorithm. The vessel segmentation experiments compared with previous geometric active contour models are shown on several medical images. The experimental results demonstrate the performance of our approach.

Clinical validation of three-dimensional tortuosity metrics based on the minimum curvature of approximating polynomial splines

The clinical recognition of abnormal vascular tortuosity is important in the diagnosis of many diseases. Metrics based on three-dimensional (3D) curvature, using approximating polynomial spline-fitting to "data balls" centered along the mid-line of the vessel, minimize digitization errors and give tortuosity values largely independent of the resolution of the imaging system. We applied two of these metrics to a number of clinical vascular systems, using both 2D and 3D datasets. Using abdominal aortograms of low tortuosity, we established their validity by their strong correlation with the ranking of an expert panel of three vascular surgeons. The values of the Spearman rank correlation coefficient between our rankings, using a data ball radius of one-quarter of the local vessel radius, and the average ranking of the expert panel were 0.96 (with a 95% confidence interval of [0.91, 0.99]) for the mean curvature and 0.98 ([0.94, 0.99]) for the root-mean-square (RMS) curvature. These confidence intervals indicate that our automated analysis is producing rankings whose reliability is similar to that of a human expert, and is significantly better than that achieved with existing algorithms. The metrics provided good discrimination between vessels of different tortuosity for both 2D and 3D datasets, and produced values sufficiently discriminating to assess the relative utility of arteries for endoluminal repair of aneurysms.

REVIEW - a reference data set for retinal vessel profiles

This paper describes REVIEW, a new retinal vessel reference dataset. This dataset includes 16 images with 193 vessel segments, demonstrating a variety of pathologies and vessel types. The vessel edges are marked by three observers using a special drawing tool. The paper also describes the algorithm used to process these segments to produce vessel profiles, against which vessel width measurement algorithms can be assessed. Recommendations are given for use of the dataset in performance assessment. REVIEW can be downloaded from http://ReviewDB.lincoln.ac.uk.

Blood vessels and feature points detection on retinal images

In this paper we present a method for the automatic extraction of blood vessels from retinal images, while capturing points of intersection/overlap and endpoints of the vascular tree. The algorithm performance is evaluated through a comparison with handmade segmented images available on the STARE project database (STructured Analysis of the REtina). The algorithm is performed on the green channel of the RGB triad. The green channel can be used to represent the illumination component. The matched filter is used to enhance vessels w.r.t. the background. The separation between vessels and background is accomplished by a threshold operator based on gaussian probability density function. The length filtering removes pixels and isolated segments from the resulting image. Finally endpoints, intersections and overlapping vessels are extracted.

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

This paper presents an automated method to identify arteries and veins in dual-wavelength retinal fundus images recorded at 570 and 600 nm. Dual-wavelength imaging provides both structural and functional features that can be exploited for identification. The processing begins with automated tracing of the vessels from the 570-nm image. The 600-nm image is registered to this image, and structural and functional features are computed for each vessel segment. We use the relative strength of the vessel central reflex as the structural feature. The central reflex phenomenon, caused by light reflection from vessel surfaces that are parallel to the incident light, is especially pronounced at longer wavelengths for arteries compared to veins. We use a dual-Gaussian to model the cross-sectional intensity profile of vessels. The model parameters are estimated using a robust M-estimator, and the relative strength of the central reflex is computed from these parameters. The functional feature exploits the fact that arterial blood is more oxygenated relative to that in veins. This motivates use of the ratio of the vessel optical densities (ODs) from images at oxygen-sensitive and oxygen-insensitive wavelengths (ODR = OD600/OD570) as a functional indicator. Finally, the structural and functional features are combined in a classifier to identify the type of the vessel. We experimented with four different classifiers and the best result was given by a support vector machine (SVM) classifier. With the SVM classifier, the proposed algorithm achieved true positive rates of 97% for the arteries and 90% for the veins, when applied to a set of 251 vessel segments obtained from 25 dual wavelength images. The ability to identify the vessel type is useful in applications such as automated retinal vessel oximetry and automated analysis of vascular changes without manual intervention.

Segmentation of blood vessels from red-free and fluorescein retinal images

The morphology of the retinal blood vessels can be an important indicator for diseases like diabetes, hypertension and retinopathy of prematurity (ROP). Thus, the measurement of changes in morphology of arterioles and venules can be of diagnostic value. Here we present a method to automatically segment retinal blood vessels based upon multiscale feature extraction. This method overcomes the problem of variations in contrast inherent in these images by using the first and second spatial derivatives of the intensity image that gives information about vessel topology. This approach also enables the detection of blood vessels of different widths, lengths and orientations. The local maxima over scales of the magnitude of the gradient and the maximum principal curvature of the Hessian tensor are used in a multiple pass region growing procedure. The growth progressively segments the blood vessels using feature information together with spatial information. The algorithm is tested on red-free and fluorescein retinal images, taken from two local and two public databases. Comparison with first public database yields values of 75.05% true positive rate (TPR) and 4.38% false positive rate (FPR). Second database values are of 72.46% TPR and 3.45% FPR. Our results on both public databases were comparable in performance with other authors. However, we conclude that these values are not sensitive enough so as to evaluate the performance of vessel geometry detection. Therefore we propose a new approach that uses measurements of vessel diameters and branching angles as a validation criterion to compare our segmented images with those hand segmented from public databases. Comparisons made between both hand segmented images from public databases showed a large inter-subject variability on geometric values. A last evaluation was made comparing vessel geometric values obtained from our segmented images between red-free and fluorescein paired images with the latter as the "ground truth". Our results demonstrated that borders found by our method are less biased and follow more consistently the border of the vessel and therefore they yield more confident geometric values.

Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et al., 2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction

This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.

Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy

A fully automated approach is presented for robust detection and classification of changes in longitudinal time-series of color retinal fundus images of diabetic retinopathy. The method is robust to: 1) spatial variations in illumination resulting from instrument limitations and changes both within, and between patient visits; 2) imaging artifacts such as dust particles; 3) outliers in the training data; 4) segmentation and alignment errors. Robustness to illumination variation is achieved by a novel iterative algorithm to estimate the reflectance of the retina exploiting automatically extracted segmentations of the retinal vasculature, optic disk, fovea, and pathologies. Robustness to dust artifacts is achieved by exploiting their spectral characteristics, enabling application to film-based, as well as digital imaging systems. False changes from alignment errors are minimized by subpixel accuracy registration using a 12-parameter transformation that accounts for unknown retinal curvature and camera parameters. Bayesian detection and classification algorithms are used to generate a color-coded output that is readily inspected. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 97% change detection rate, a 3% miss rate, and a 10% false alarm rate. The performance in correctly classifying the changes was 99.3%. A self-consistency metric, and an error factor were developed to measure performance over more than two periods. The average self consistency was 94% and the error factor was 0.06%. Although this study focuses on diabetic changes, the proposed techniques have broader applicability in ophthalmology.

On the Adaptive Detection of Blood Vessels in Retinal Images

This paper proposes an automated blood vessel detection scheme based on adaptive contrast enhancement, feature extraction, and tracing. Feature extraction of small blood vessels is performed by using the standard deviation of Gabor filter responses. Tracing of vessels is done via forward detection, bifurcation identification, and backward verification. Tests over twenty images show that for normal images, the true positive rate (TPR) ranges from 80% to 91%, and their corresponding false positive rates (FPR) range from 2.8% to 5.5%. For abnormal images, the TPR ranges from 73.8% to 86.5% and the FPR ranges from 2.1% to 5.3%, respectively. In comparison with two published solution schemes that were also based on the STARE database, our scheme has lower FPR for the reported TPR measure.

A 2-D/3-D model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy

This paper presents model-based information-theoretic methods to quantify the complexity of tumor microvasculature, taking into account shape, textural, and structural irregularities. The proposed techniques are completely automated, and are applicable to optical slices (3-D) or projection images (2-D). Improvements upon the prior literature include: (i) measuring local (vessel segment) as well as global (entire image) vascular complexity without requiring explicit segmentation or tracing; (ii) focusing on the vessel boundaries in the complexity estimate; and (iii) added robustness to image artifacts common to tumor microvasculature images. Vessels are modeled using a family of super-Gaussian functions that are based on the superquadric modeling primitive common in computer vision. The superquadric generalizes a simple ellipsoid by including shape parameters that allow it to approximate a cylinder with elliptical cross-sections (generalized cylinder). The super-Gaussian is obtained by composing a superquadric with an exponential function giving a form that is similar to a standard Gaussian function but with the ability to produce level sets that approximate generalized cylinders. Importantly, the super-Gaussian is continuous and differentiable so it can be fit to image data using robust non-linear regression. This fitting enables quantification of the intrinsic complexity of vessel data vis-a-vis the super-Gaussian model within a minimum message length (MML) framework. The resulting measures are expressed in units of information (bits). Synthetic and real-data examples are provided to illustrate the proposed measures.

Algorithms for automated oximetry along the retinal vascular tree from dual-wavelength fundus images

We present an automated method to perform accurate, rapid, and objective measurement of the blood oxygen saturation over each segment of the retinal vascular hierarchy from dual-wavelength fundus images. Its speed and automation (2 s per entire image versus 20 s per segment for manual methods) enables detailed level-by-level measurements over wider areas. An automated tracing algorithm is used to estimate vessel centerlines, thickness, directions, and locations of landmarks such as bifurcations and crossover points. The hierarchical structure of the vascular network is recovered from the trace fragments and landmarks by a novel algorithm. Optical densities (OD) are measured from vascular segments using the minimum reflected intensities inside and outside the vessel. The OD ratio (ODR=OD600/OD570) bears an inverse relationship to systemic HbO2 saturation (SO2). The sensitivity for detecting saturation change when breathing air versus pure oxygen was calculated from the measurements made on six subjects and was found to be 0.0226 ODR units, which is in good agreement with previous manual measurements by the dual-wavelength technique, indicating the validity of the automation. A fully automated system for retinal vessel oximetry would prove useful to achieve early assessments of risk for progression of disease conditions associated with oxygen utilization.

Analyzing attributes of vessel populations

Almost all diseases affect blood vessel attributes (vessel number, radius, tortuosity, and branching pattern). Quantitative measurement of vessel attributes over relevant vessel populations could thus provide an important means of diagnosing and staging disease. Unfortunately, little is known about the statistical properties of vessel attributes. In particular, it is unclear whether vessel attributes fit a Gaussian distribution, how dependent these values are upon anatomical location, and how best to represent the attribute values of the multiple vessels comprising a population of interest in a single patient. The purpose of this report is to explore the distributions of several vessel attributes over vessel populations located in different parts of the head. In 13 healthy subjects, we extract vessels from MRA data, define vessel trees comprising the anterior cerebral, right and left middle cerebral, and posterior cerebral circulations, and, for each of these four populations, analyze the vessel number, average radius, branching frequency, and tortuosity. For the parameters analyzed, we conclude that statistical methods employing summary measures for each attribute within each region of interest for each patient are preferable to methods that deal with individual vessels, that the distributions of the summary measures are indeed Gaussian, and that attribute values may differ by anatomical location. These results should be useful in designing studies that compare patients with suspected disease to a database of healthy subjects and are relevant to groups interested in atlas formation and in the statistics of tubular objects.