Quantitative measurement of changes in retinal vessel diameter in ocular fundus images
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Algorithm for computer control of a digital plotter
IBM Systems Journal
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Automatic retinal image registration scheme using global optimization techniques
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
IEEE Transactions on Image Processing
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
Automatic vessel network features quantification using local vessel pattern operator
Computers in Biology and Medicine
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An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.