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13
A Review of Vessel Extraction Techniques and Algorithms
 ACM Computing Surveys
, 2000
"... 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 r ..."
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Cited by 183 (0 self)
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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) modelbased approaches, (3) trackingbased approaches, (4) artificial intelligencebased approaches, (5) neural networkbased approaches, and (6) miscellaneous tubelike 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, preprocessing, user interaction, and result type.
Rapid Automated Tracing and Feature Extraction from Retinal Fundus Images Using Direct Exploratory Algorithms
 IEEE Trans. Inform. Technol. Biomed
, 1999
"... 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 ..."
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Cited by 107 (20 self)
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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/underexposure, 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 deadlinedriven 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 graylevel data without any preprocessing, and from processing only a minimally necessary fraction of pixels in an exploratory manner, avoiding lowlevel imagewide operations such as thresholding, edge detection, and morphological processing. These properties make the algorithm suited to realtime, online (live) processing and is being applied to computerassisted laser retinal surgery.
Rapid automated threedimensional tracing of neurons from confocal image stacks
 IEEE Transactions on Information Technology in Biomedicine
, 2002
"... Abstract—Algorithms are presented for fully automatic threedimensional (3D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxelbased skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of R dire ..."
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Cited by 83 (14 self)
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Abstract—Algorithms are presented for fully automatic threedimensional (3D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxelbased skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of R directional kernels (e.g., a QP), guided by a generalized 3D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3D space. Since the centerlines are of primary interest, the 3D 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, graphtheoretic 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 70MB image on a 500MHz computer. These properties make it attractive for largescale automated tissue studies that require rapid online image analysis, such as highthroughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project. Index Terms—Aotomated morphometry, micrograph analysis, neuron tracint, threedimensional (3D) image filtering, threedimensional (3D) vectorization. I.
Optimal scheduling of tracing computations for realtime vascular landmark extraction from retinal fundus images
 IEEE Transactions on Information Technology in Biomedicine
, 2001
"... 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 featurebased image matching for controlling a co ..."
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Cited by 30 (15 self)
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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 featurebased image matching for controlling a computerassisted 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 gridbased 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 realtime 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
Robust modelbased vasculature detection in noisy biomedical images
 IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
, 2004
"... This paper presents a set of algorithms for robust detection of vasculature in noisy retinal video images. Three methods are studied for effective handling of outliers. The first method is based on Huber’s censored likelihood ratio test. The second is based on the use of atrimmed test statistic. T ..."
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Cited by 16 (4 self)
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This paper presents a set of algorithms for robust detection of vasculature in noisy retinal video images. Three methods are studied for effective handling of outliers. The first method is based on Huber’s censored likelihood ratio test. The second is based on the use of atrimmed test statistic. The third is based on robust model selection algorithms. All of these algorithms rely on a mathematical model for the vasculature that accounts for the expected variations in intensity/texture profile, width, orientation, scale, and imaging noise. These unknown parameters are estimated implicitly within a robust detection and estimation framework. The proposed algorithms are also useful as nonlinear vessel enhancement filters. The proposed algorithms were evaluated over carefully constructed phantom images, where the ground truth is known a priori, as well as clinically recorded images for which the ground truth was manually compiled. A comparative evaluation of the proposed approaches is presented. Collectively, these methods outperformed prior approaches based on Chaudhuri et al. (1989) matched filtering, as well as the verification methods used by prior exploratory tracing algorithms, such as the work of Can et al. (1999). The Huber censored likelihood test yielded the best overall improvement, with a 145.7 % improvement over the exploratory tracing algorithm, and a 43.7 % improvement in detection rates over the matched filter.
Predictive scheduling algorithms for realtime feature extraction and spatial referencing: Application to retinal image sequences
 IEEE Transactions on Biomedical Engineering
, 2002
"... Abstract—Realtime spatial referencing is an important alternative to tracking for designing spatially aware ophthalmic instrumentation for procedures such as laser photocoagulation and perimetry. It requires independent, fast registration of each image frame from a digital video stream (1024 1024 p ..."
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Cited by 9 (6 self)
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Abstract—Realtime spatial referencing is an important alternative to tracking for designing spatially aware ophthalmic instrumentation for procedures such as laser photocoagulation and perimetry. It requires independent, fast registration of each image frame from a digital video stream (1024 1024 pixels) to a spatial map of the retina. Recently, we have introduced a spatial referencing algorithm that works in three primary steps: 1) tracing the retinal vasculature to extract image feature (landmarks); 2) invariant indexing to generate hypothesized landmark correspondences and initial transformations; and 3) alignment and verification steps to robustly estimate a 12parameter quadratic spatial transformation between the image frame and the map. The goal of this paper is to introduce techniques to minimize the amount of computation for successful spatial referencing. The fundamental driving idea is to make feature extraction subservient to registration and, therefore, only produce the information needed for verified, accurate transformations. To this end, the image is analyzed along onedimensional, vertical and horizontal grid lines to produce a regular sampling of the vasculature, needed for step 3) and to initiate step 1). Tracing of the vascular is then prioritized hierarchically to quickly extract landmarks and groups (constellations) of landmarks for indexing. Finally, the tracing and spatial referencing computations are integrated so that landmark constellations found by tracing are tested immediately. The resulting implementation is an orderofmagnitude faster with the same success rate. The average total computation time is 31.2 ms per image on a 2.2GHz Pentium Xeon processor. Index Terms—Biomedical image analysis, featurebased image registration, mosaic synthesis, realtime computing, retinal image sequences, scheduling, spatial mapping, spatial referencing, vasculature tracing. I.
Fast and Accurate Retinal Vasculature Tracing and KernelIsomapbased Feature Selection
"... AbstractThe blood vessels in the retina have a characteristic radiating pattern, while there exists a significant variation dependent on the individual and/or medical condition. Extracting the geometric properties of these blood vessels have several important applications, such as biometrics (for i ..."
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Cited by 2 (1 self)
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AbstractThe blood vessels in the retina have a characteristic radiating pattern, while there exists a significant variation dependent on the individual and/or medical condition. Extracting the geometric properties of these blood vessels have several important applications, such as biometrics (for identification) and medical diagnosis. In this paper, we will focus on biometric applications. For this, we propose a fast and accurate algorithm for tracing the blood vessels, and compare several candidate summary features based on the tracing results. Existing tracing algorithms based on a detailed analysis of the image can be too slow to quickly process a large volume of retinal images in real time (e.g., at a security check point). In order to select good features that can be extracted from the traces, we used kernel Isomap to test the distance between different retinal images as projected onto their respective feature spaces. We tested the following feature set: (1) angle among branches, (2) the number of fiber based on distance, (3) distance between branches, and (4) inner product among branches. Our results indicate that features 3 and 4 are prime candidates for use in fast, realtime biometric tasks. We expect our method to lead to fast and accurate biometric systems based on retinal images. I.
MAXMIN CENTRAL VEIN DETECTION IN RETINAL FUNDUS IMAGES
"... This paper describes a new framework for the automated tracking of the central retinal vein in retinal images. The procedure first computes a binary image of the retinal vasculature, then obtains the skeleton (medial axis) of the vascular network. Terminal and branching points of the network are ..."
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Cited by 2 (0 self)
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This paper describes a new framework for the automated tracking of the central retinal vein in retinal images. The procedure first computes a binary image of the retinal vasculature, then obtains the skeleton (medial axis) of the vascular network. Terminal and branching points of the network are then located, and the network converted into a graph representation including length and thickness information for all vessels. Finally, a MaxMin approach is used to locate the central vein:The candidates central vein are the minimal paths from the optic disk to all terminal nodes found using Dijkstra algorithm. The actual central vein is selected among the all candidates by maximizing a merit function estimating the total vessel area in the image. Results are presented and compared with those provided by a manual classification on 20 images of the DRIVE set. An overall performance ratio of 92 % is achieved. Index Terms Retinal, central, vein, vessel, graph 1.
Automated Analysis of Longitudinal Changes in Color Retinal Fundus Images for Monitoring Diabetic Retinopathy," Accepted for publication in the
 IEEE Transactions on Biomedical Engineering
"... Automated image analysis algorithms are presented for detection and classification of changes in longitudinal timeseries of color retinal fundus images. They are applicable to clinical practice, quantitative scoring of clinical trials, computerassisted reading centers, and training. This work focu ..."
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Cited by 1 (1 self)
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Automated image analysis algorithms are presented for detection and classification of changes in longitudinal timeseries of color retinal fundus images. They are applicable to clinical practice, quantitative scoring of clinical trials, computerassisted reading centers, and training. This work focuses on diabetesrelated changes, although the techniques have broader applicability. Retinal features, including the vasculature, vessel branching/crossover locations, optic disk, and fovea are extracted automatically. The images are registered to subpixel accuracy using a 12dimensional mapping that accounts for the unknown retinal curvature and camera parameters. The images are corrected for nonuniform illumination using a robust homomorphic surface fitting algorithm. The changes in nonvascular regions are segmented using an algorithm that is robust to relevant artifacts such as dust particles in the optical path. They are classified into five clinically significant categories using a Bayesian algorithm constrained by Markov Random Fields. A flicker animation overlaid with change analysis results allows qualitative and quantitative assessment by the user. A multiobserver validation on 43 image pairs from 22 eyes involving nonproliferative and proliferative diabetic retinopathies, showed a 96.83 % change detection rate, a 3.17 % miss rate, and a 17.65 % false alarm rate. The performance in correctly classifying the changes was 97.39 %.