Algorithms are presented for rapid, automatic, robust, adaptive, and accurate tracing of retinal vasculature and analysis of intersections and crossovers. This method improves upon prior work in several ways: 1) automatic adaptation from frame to frame without manual initialization/adjustment, with few tunable parameters; 2) robust operation on image sequences exhibiting natural variability, poor and varying imaging conditions, including over/under-exposure, low contrast, and artifacts such as glare; 3) does not require the vasculature to be connected, so it can handle partial views; and 4) operation is efficient enough for use on unspecialized hardware, and amenable to deadline-driven computing, being able to produce a rapidly and monotonically improving sequence of usable partial results. Increased computation can be traded for superior tracing performance. Its efficiency comes from direct processing on gray-level data without any preprocessing, and from processing only a minimally necessary fraction of pixels in an exploratory manner, avoiding low-level image-wide operations such as thresholding, edge detection, and morphological processing. These properties make the algorithm suited to real-time, on-line (live) processing and is being applied to computer-assisted laser retinal surgery.

Abstract—We describe an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. Our method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. We evaluate our method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that our method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75 % true positive rate. For a baseline, we also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4 % false positive detection rate, on average. These numbers suggest there is still room for a 15 % true positive rate improvement, with the same false positive rate, over our method. We are making all our images and hand labelings publicly available for interested researchers to use in evaluating related methods. Index Terms—Adaptive thresholding, blood vessel segmentation, matched filter, retinal imaging. I.

Vessel segmentation algorithms are the critical components of circulatory blood vessel analysis systems. We present a survey of vessel extraction techniques and algorithms. We put the various vessel extraction approaches and techniques in perspective by means of a classification of the existing research. While we have mainly targeted the extraction of blood vessels, neurosvascular structure in particular, we have also reviewed some of the segmentation methods for the tubular objects that show similar characteristics to vessels. We have divided vessel segmentation algorithms and techniques into six main categories: (1) pattern recognition techniques, (2) model-based approaches, (3) tracking-based approaches, (4) artificial intelligence-based approaches, (5) neural network-based approaches, and (6) miscellaneous tube-like object detection approaches. Some of these categories are further divided into sub- categories. We have also created tables to compare the papers in each category against such criteria as dimensionality, input type, pre-processing, user interaction, and result type.

Abstract—Algorithms are presented for fully automatic three-dimensional (3-D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxel-based skeletonization methods, the present approach works by recur-sively following the neuronal topology, using a set of R directional kernels (e.g., a QP), guided by a generalized 3-D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3-D space. Since the centerlines are of primary interest, the 3-D extension can be accomplished by four rather than six sets of kernels. Additional modifications, such as dynamic adaptation of the correlation kernels, and adaptive step size estimation, were introduced for achieving robustness to photon noise, varying contrast, and apparent discontinuity and/or hollowness of structures. The end product is a labeling of all somas present, graph-theoretic representations of all dendritic/axonal structures, and image statistics such as soma volume and centroid, soma interconnectivity, the longest branch, and lengths of all graph branches originating from a soma. This method is able to work directly with unprocessed confocal images, without expensive deconvolution or other preprocessing. It is much faster that skeletonization, typically consuming less than a minute to trace a 70-MB image on a 500-MHz computer. These properties make it attractive for large-scale automated tissue studies that require rapid on-line image analysis, such as high-throughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project. Index Terms—Aotomated morphometry, micrograph analysis, neuron tracint, three-dimensional (3-D) image filtering, three-dimensional (3-D) vectorization. I.

The new therapeutic method of `scotoma-based photocoagulation' developed at the Vienna Eye Clinic for diagnosis and treatment of age-related macular degeneration requires retinal maps from scanning laser ophthalmoscope images. This paper describes in detail all necessary image analysis steps for map generation. A prototype software system for fully automatic map generation has been implemented and tested on a representative dataset selected from a clinical study with 50 patients. The map required for the scotoma-based photocoagulation treatment can be reliably extracted in all cases. Thus, algorithms presented in this paper should be directly applicable in daily clinical routine without major modifications. Keywords--- Ophthalmology, feature extraction, registration, visualization I. Motivation T HERE is a strong medical motivation for the work presented in this paper: Age-related macular degeneration (AMD) is the main reason for often severe loss and lasting decrease in visual acu...

Recently, this group published fast algorithms for automatic tracing (vectorization) of the vasculature in live retinal angiograms, and for the extraction of visual landmarks formed by vascular bifurcations and crossings. These landmarks are used for feature-based image matching for controlling a computer-assisted laser retinal surgery instrument currently under development. This paper describes methods to schedule the vascular tracing computations to maximize the rate of growth of quality of the partial tracing results within a frame cycle. There are two main advantages. First, progressive image matching from partially extracted landmark sets can be faster, and provide an earlier indication of matching failure. Second, the likelihood of successful image matching is greatly improved since the extracted landmarks are of the highest quality for the given computational budget. The scheduling method is based on quantitative measures for the computational work and the quality of landmarks. A coarse grid-based analysis of the image is used to generate seed points for the tracing computations, along with estimates of local edge strengths, orientations, and vessel thickness. These estimates are used to define criteria for real-time preemptive scheduling of the tracing computations. It is shown that the optimal schedule can only be achieved in perfect hindsight, and is thus unrealizable. This leads to scheduling heuristics that approximate the behavior of the optimal algorithm. One such approximation produced ≈400 % improvement in the

Abstract—In this paper, the fitness of estimating vessel profiles with Gaussian function is evaluated and an amplitude-modified second-order Gaussian filter is proposed for the detection and measurement of vessels. Mathematical analysis is given and supported by a simulation and experiments to demonstrate that the vessel width can be measured in linear relationship with the “spreading factor ” of the matched filter when the magnitude coefficient of the filter is suitably assigned. The absolute value of vessel diameter can be determined simply by using a precalibrated line, which is typically required since images are always system dependent. The experiment shows that the inclusion of the width measurement in the detection process can improve the performance of matched filter and result in a significant increase in success rate of detection. Index Terms—Fundus image, matched filter, retinal vessel, vessel measurements. I.

Although laser retinal surgery is the best available treatment for choridal neovascularization, the current procedure has a low success rate (50%). Challenges, such as motioncompensated beam steering, ensuring complete coverage and minimizing incidental photodamage, can be overcome with improved instrumentation. This paper presents core image processing algorithms for 1) rapid identification of branching and crossover points of the retinal vasculature; 2) automatic montaging of video retinal angiograms; 3) real-time location determination and tracking using a combination of feature-tagged point-matching and dynamic-pixel templates. These algorithms tradeoff conflicting needs for accuracy, robustness to image variations (due to movements and the difficulty of providing steady illumination) and noise, and operational speed in the context of available hardware. The algorithm for locating vasculature landmarks performed robustly at a speed of 16--30 video image frames/s depending upon the field on a Silicon Graphics workstation. The montaging algorithm performed at a speed of 1.6--4 s for merging 5--12 frames. The tracking algorithm was validated by manually locating six landmark points on an image sequence with 180 frames, demonstrating a mean-squared error of 1.35 pixels. It successfully detected and rejected instances when the image dimmed, faded, lost contrast, or lost focus.

In this paper we present a algorithm for the real-time enhancement and detection of blood vessels in medical images. The algorithm is based on a set of linear lters sensitive to vessels of dierent orientation and thickness. Such lters are obtained as linear combinations of properly shifted Gaussian kernels. The output of multiple oriented vessel-enhancing lters can be integrated to obtain images in which vessels are highly enhanced independently of their direction and thickness. To avoid spurious responses in the presence of step edges or to enhance the skeletons of vessels, the output of directional lters can be validated before integration. Skeleton detection and vessel segmentation can be performed via thresholding with hysteresis. Experimental results on synthetic images and real coronary arteriograms are reported. Keywords Vessel Enhancement, Vessel Detection, Realtime Processing, Skeleton Detection, Vessel Segmentation. Address for Correspondence Riccardo Poli...

Abstract—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. Index Terms—Fundus, Gabor, pattern classification, retina, vessel segmentation, wavelet.