Journal of Signal Processing Systems
A Statistical Approach for Detecting Tubular Structures in Myocardial Infarct Scars
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Distance regression by Gauss–Newton-type methods and iteratively re-weighted least-squares
Computing - Geometric Modelling, Dagstuhl 2008
FABC: retinal vessel segmentation using adaboost
IEEE Transactions on Information Technology in Biomedicine
Retinal vessel segmentation using a probabilistic tracking method
Pattern Recognition
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
An approach to localize the retinal blood vessels using bit planes and centerline detection
Computer Methods and Programs in Biomedicine
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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 a α -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.