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Robust parameter estimation in computer vision
 SIAM Reviews
, 1999
"... Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite smallscale noise in the data, occasional largescale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techni ..."
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Cited by 129 (10 self)
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Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite smallscale noise in the data, occasional largescale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techniques, some borrowed from the statistics literature and others described in the computer vision literature, have been used in solving these parameter estimation problems. Ideally, these techniques should effectively ignore the outliers and measurements from other populations, treating them as outliers, when estimating the parameters of a single population. Two frequently used techniques are leastmedian of
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 79 (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.
The dualbootstrap iterative closest point algorithm with application to retinal image registration
 IEEE Trans. Med. Img
, 2003
"... Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called DualBootstrap Iterative Closest Point (DualBootstrap ICP). The approach is to start from one or more initial, loworder estimates that are only accurate in small ..."
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Cited by 57 (18 self)
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Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called DualBootstrap Iterative Closest Point (DualBootstrap ICP). The approach is to start from one or more initial, loworder estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, DualBootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5 % of the pairs containing at least one common landmark, and 100 % of the pairs containing at least one common landmark and at least 35 % image overlap. Index Terms—Iterative closest point, medical imaging, registration, retinal imaging, robust estimation.
A featurebased, robust, hierarchical algorithm for registering pairs of images of the curved human retina
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2002
"... AbstractÐThis paper describes a robust hierarchical algorithm for fullyautomatic registration of a pair of images of the curved human retina photographed by a fundus microscope. Accurate registration is essential for mosaic synthesis, change detection, and design of computeraided instrumentation. ..."
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Cited by 57 (18 self)
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AbstractÐThis paper describes a robust hierarchical algorithm for fullyautomatic registration of a pair of images of the curved human retina photographed by a fundus microscope. Accurate registration is essential for mosaic synthesis, change detection, and design of computeraided instrumentation. Central to the newalgorithm is a 12parameter interimage transformation derived by modeling the retina as a rigid quadratic surface with unknown parameters, imaged by an uncalibrated weak perspective camera. The parameters of this model are estimated by matching vascular landmarks extracted by an algorithm that recursively traces the blood vessel structure. The parameter estimation technique, which could be generalized to other applications, is a hierarchy of models and methods: an initial match set is pruned based on a zeroth order transformation estimated as the peak of a similarityweighted histogram; a first order, affine transformation is estimated using the reduced match set and leastmedian of squares; and the final, second order, 12parameter transformation is estimated using an Mestimator initialized from the first order estimate. This hierarchy makes the algorithm robust to unmatchable image features and mismatches between features caused by large interframe motions. Before final convergence of the Mestimator, feature positions are refined and the correspondence set is enhanced using normalized sumofsquared differences matching of regions deformed by the emerging transformation. Experiments involving 3,000 image pairs �1; 024 1; 024 pixels) from 16 different healthy eyes were performed. Starting with as low as 20 percent overlap between images, the algorithm improves its success rate exponentially and has a negligible failure rate above 67 percent overlap. The experiments also quantify the reduction in errors as the model complexities increase. Final registration errors less than a pixel are routinely achieved. The speed, accuracy, and
FrameRate Spatial Referencing Based on Invariant Indexing and Alignment with Application to OnLine Retinal Image Registration
, 2002
"... This paper describes an algorithm to continually and accurately estimate the absolute location of a diagnostic or surgical tool (such as a laser) pointed at the human retina, from a series of image frames. We treat the problem as a registration problem, using diagnostic images to build a spatial map ..."
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Cited by 16 (10 self)
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This paper describes an algorithm to continually and accurately estimate the absolute location of a diagnostic or surgical tool (such as a laser) pointed at the human retina, from a series of image frames. We treat the problem as a registration problem, using diagnostic images to build a spatial map of the retina and then registering each online against this map. Since the image location where the laser strikes the retina is easily found, this registration determines the position of the laser in the global coordinate system defined by the spatial map. For each online image, the algorithm computes similarity invariants, locally valid despite the curved nature of the retina, from constellations of vascular landmarks. These are detected using a highspeed algorithm that iteratively traces the blood vessel structure. Invariant indexing establishes initial correspondences between landmarks from the online image and landmarks stored in the spatial map. Robust alignment and verification steps extend the similarity transformation computed from these initial correspondences to a global, highorder transformation. In initial experimentation, the method has achieved 100% success on 1024 1024 retina images. With a version of the tracing algorithm optimized for speed on 512 512 images, the computation time is only 51 milliseconds per image on a 900MHz Pentium III processor and a 97% success rate is achieved. The median registration error in either case is about 1 pixel.
Robust Hierarchical Algorithm for Constructing a Mosaic from Images of the Curved Human Retina
, 1999
"... This paper describes computer vision algorithms to assist in retinal laser surgery, which is widely used to treat leading blindness causing conditions but only has a 50% success rate, mostly due to a lack of spatial mapping and reckoning capabilities in current instruments. The novel technique descr ..."
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Cited by 16 (6 self)
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This paper describes computer vision algorithms to assist in retinal laser surgery, which is widely used to treat leading blindness causing conditions but only has a 50% success rate, mostly due to a lack of spatial mapping and reckoning capabilities in current instruments. The novel technique described here automatically constructs a composite (mosaic) image of the retina from a sequence of incomplete views. This mosaic will be useful to opthalmologists for both diagnosis and surgery. The new technique goes beyond published methods in both the medical and computer vision literatures because it is fully automated, models the patientdependent curvature of the retina, handles large interframe motions, and does not require calibration. At the heart of the technique is a 12parameter image transformation model derived by modeling the retina as a quadratic surface and assuming a weak perspective camera, and rigid motion. Estimating the parameters of this transformation model requires robus...
A FeatureBased Technique for Joint, Linear Estimation of HighOrder Imageto Mosaic Transformations: Application to Mosaicing the Curved Human Retina
 In Proceedings IEEE Conference on Computer Vision and Pattern Recognition
, 2000
"... Methods are presented for increasing the coverage and accuracy of image mosaics constructed from multiple, uncalibrated, weakperspective views of the human retina. Extending our previous algorithm for registering pairs of image using a noninvertible, 12parameter, quadratic image transformation mo ..."
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Cited by 14 (2 self)
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Methods are presented for increasing the coverage and accuracy of image mosaics constructed from multiple, uncalibrated, weakperspective views of the human retina. Extending our previous algorithm for registering pairs of image using a noninvertible, 12parameter, quadratic image transformation model and a hierarchical, robust estimation technique, two important innovations are presented. (1) The first is a linear, noniterative method for jointly estimating the transformations of all images onto the mosaic. This employs constraints derived from pairwise matching between the nonmosaic image frames. It allows the transformations to be estimated for images that do not overlap the mosaic anchor frame, and results in mutually consistent transformations for all images. This means the mosaics can cover a much broader area of the retinal surface, even though the transformation model is not closed under composition. This capability is particularly valuable for mosaicing the retinal periphery in the context of diseases such as AIDS/CMV. (2) The second innovation is a method to improve the accuracy of the pairwise matches as well as the joint estimation by refining the feature locations and by adding new features based on the transformation estimates themselves. For matching image frames of size 1024 1024, this cuts the registration error from the range of 1 to 3 pixels to about 0.55 pixels. The overall transformation error in final mosaic construction is 0.80 pixels based on experiments over a large set of eyes.
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 11 (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
, 2002
"... Realtime spatial referencing is an important alternative to tracking for designing spatiallyaware 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) t ..."
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Cited by 9 (6 self)
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Realtime spatial referencing is an important alternative to tracking for designing spatiallyaware 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 veri cation 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 veri ed, 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 45.5 milliseconds per image on a 2.2 GHz Pentium Xeon processor.
A Partial Intensity Invariant Feature Descriptor for Multimodal Retinal Image Registration
, 2009
"... Abstract—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 descri ..."
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Cited by 1 (0 self)
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Abstract—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 HarrisPIIFD. PIIFD is invariant to image rotation, partially invariant to image intensity, affine transformation, and viewpoint/perspective change. Our HarrisPIIFD 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