A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A buyer's guide to conic fitting
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Randomized Hough transform: improved ellipse detection with comparison
Pattern Recognition Letters
Computed tomography angiography: a case study of peripheral vessel investigation
Proceedings of the conference on Visualization '01
An approach for detecting blood vessel diseases from cone-beam CT image
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Equivalence of subpixel motion estimators based on optical flow and block matching
ISCV '95 Proceedings of the International Symposium on Computer Vision
IEEE Transactions on Image Processing
Non-Linear Model Fitting to Parameterize Diseased Blood Vessels
VIS '04 Proceedings of the conference on Visualization '04
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Accurate determination of the vessel axis is a prerequisite for automated visualization and quantification of artery diseases. This paper presents an evaluation of different methods for approximating the centerline of the vessel in a phantom simulating the peripheral arteries. Six algorithms were used to determine the centerline of a synthetic peripheral arterial vessel. They are based on: ray casting using thresholds and maximum gradient-like stop criterion, pixel motion estimation between successive images called block matching, center of gravity and shape based segmentation. The Randomized Hough Transform and ellipse fitting have been used as shape based segmentation techniques. Since in the synthetic data set the centerline is known, an estimation of the error can be calculated in order to determine the accuracy achieved by a given method.