Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
Dense Non-Rigid Motion Estimation in Sequencesof Medical Images Using Differential Constraints
International Journal of Computer Vision
Image Registration for Digital Subtraction Angiography
International Journal of Computer Vision
2D and 3D Motion Analysis in Digital Subtraction Angiography
CVRMed '95 Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Robust surface matching for registration
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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The aim of this work is to develop a method of automatic data collection for coronary blood flow estimation based on computer analysis of angiographic image sequences. In the methods of coronary flow measurements, apart from problems involving densitometric analysis, there is also the problem of cyclic movement of the measurement field, i.e. of an artery segment or part of the myocardium. The system of automatic artery segment tracking presented in this paper does not only reduce the "manual effort" of the operator to establish the region of interest in the frame sequence, but also makes it possible to plot a densitometric curve with the time resolution equal to that of the frame sequence. The algorithm thus implemented, based on a template matching method, makes it also possible to trace the results of automatic detection of some characteristic points within the structure of arteries and to correct any faulty matching. The incorporation of the movement trajectories obtained allows us to trace the movement of the part of the myocardium close to the characteristic points of the artery with the aim of estimating the degree of myocardium perfusion. This relatively simple algorithm is acceptable for routine clinical testing due to the short time of frame sequence analysis (few minutes) and its relatively small error (the maximum estimated error of the automatic analysis is less than 11%). The comparative analysis of the results obtained for the template matching algorithm based on several criteria of similarity failed to establish any specific criterion with regard to acceleration or matching accuracy.