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.

Abstract—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 a-trimmed 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. Index Terms—Hypothesis testing, mathematical models of vasculature, retinal fundus images, robust model selection, vasculature detection and segmentation, vessel enhancement, vessel segmentation. I.

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel centerline extraction algorithm.

This paper describes the design and implementation of an off-line, non-invasive, automated method for the examination and follow up of the arteriosclerotic changes due to hypertension, with the help of digital image processing of fundus images. This method would help in evaluating the efficacy of various treatments on the regression and reversion of arteriosclerotic lesions. This method, in interaction with appropriate knowledge bases, can be used at the clinical practice for monitoring hypertensive patients on a frequent basis, hence it aims at minimum discomfort of the patient, by-passing even the regular fluorescein injection for fundus image enhancement. Our method is based on segmenting the vasculature by identifying the centerline of each vessel utilizing the idea that vessels present a ridge in cross-sectional intensity profiles. Therefore, such a ridge can be detected along the vessels, as if there was three-dimensional information. Once the vasculature is segmented we present ...

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matched filter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A learning technique is applied to map this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the intensity Hessian show substantial improvements both qualitatively and quantitatively. When the Hessian is used in place of the matched filter, similar but less-substantial improvements are obtained. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel extraction algorithm.

Accurate measurement and identification of blood vessels could provide useful information to clinical diagnosis. A piecewise Gaussian model is proposed to describe the intensity distribution of vessel profile in this paper. The characteristic of central reflex is specially considered in the proposed model. The comparison with the single Gaussian model is performed, which shows that the piecewise Gaussian model is a more appropriate model for vessel profile. The obtained model parameters could be utilized in the identification of vessel type. The minimum Mahalanobis distance classifier is employed in the classification. 505 segments of vessels were tested. The success rate is 82.46% and 89.03% for the arteries and veins respectively.

Automated image analysis algorithms are presented for detection and classification of changes in longitudinal time-series of color retinal fundus images. They are applicable to clinical practice, quantitative scoring of clinical trials, computer-assisted reading centers, and training. This work focuses on diabetes-related 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 sub-pixel accuracy using a 12-dimensional mapping that accounts for the unknown retinal curvature and camera parameters. The images are corrected for non-uniform illumination using a robust homomorphic surface fitting algorithm. The changes in non-vascular 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 multi-observer validation on 43 image pairs from 22 eyes involving non-proliferative 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 %.

The methods presented here have been developed to perform the automated segmentation of coronary arterial structure from ciné sequences of biplanar x-ray angiograms. We introduce a methodology to impose an integrated set of constraints based on knowledge concerning the anatomical structure of the vascular system, temporal changes in position due to motion, and spatial coherence. Results are shown for data sets generated from both porcine and human studies. 1. INTRODUCTION Radiographic imaging of coronary arterial structure plays a crucial role both in diagnosing and treating patients who are at risk of heart disease 5 . In order to exploit the information generated by current clinical methods in coronary arteriography, it is necessary for the physician to build a mental model of both the three dimensional (3D) arterial structure and of the non-rigid motion that this structure undergoes as it moves with the beating heart. This mental model must be constructed from sequences of two dim...

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paired tracking for vessel extraction from retinal images