Abstract
In this paper a fully automatic method is presented for extracting blood vessel structures in poor quality coronary angiograms. The method extracts blood vessels by exploiting the spatial coherence in the image. Accurate sampling of a blood vessel requires a background elimination technique. A circular sampling technique is employed to exploit the coherence. This circular sampling technique is also applied to determine the distribution of intersection lengths between the circles and blood vessels at various threshold depths. After this sampling process, disconnected parts to the centered object are eliminated, and then the distribution of the intersection length is examined to make the decision about whether the point is on the blood vessel. To produce the final segmented image, mis-segmented noisy parts and discontinuous parts are eliminated by using angle couples and circular filtering techniques. The performance of the method is examined on various poor quality X-ray angiogram images.
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References
Coste, E., Vasseur, C., Rousseau, J.: 3D reconstruction of the cerebral arterial network from stereotactic DSA. Medical Physics 26, 1783–1793 (1999)
Kitmura, K., Tobis, J.M., Sklansky, J.: Estimating the 3D skeleton and transverse areas of coronary arteries from biplane angiograms. IEEE Trans. Med. Imaging 17, 173–187 (1988)
Eichel, P., Delph, E.J., Koral, K., Buda, A.J.: A method for fully automatic definition of coronary areterial edges from cineangiograms. IEEE Trans. Med. Imaging 7, 315–320 (1988)
Kayikcioglu, T., Gangal, A., Turhal, M.: Reconstructing coronary arterial segments from three projection boundaries. Pattern Recognition Letters 21, 611–624 (2001)
Kayikcioglu, T., Gangal, A., Turhal, M., Köse, C.: A surface based method for detection of corronary boundaries in poor quality X-ray angiogram images. Pattern Elsevier Recognition Letters 23, 783–802 (2002)
Klein, A.K., Amin, A.: Quantities coronary angiography with deformable spline models. IEEE Trans. Med. Imag. 16, 468–482 (1997)
Dhawan, A.P., Aratal, L.: Segmentation of medical images through competitive learning. Computer Methods and Programs in Biomedicine 40, 203–215 (1993)
Kirbas, C., Francis, K., Queck, H.: A review of vessel extraction Techniques and Algorithms. Vision Interfaces and Systems Laboratory, Department of Computer Science and Engineering, Wright State University, Dayton Ohio (2002)
Suri, J.S., Liu, K.C., Reden, L., Laxminarayan, S.: A review on MR vascular image processing: Skeleton versus nonskeleton approaches. IEEE Transaction on Information Technology in Biomedicine 6 (2002)
Kottke, D.P., Sun, Y.: Adaptive segmentation of coronary angiograms. In: Proc. 14th Northeast Bioeng. Conf., pp. 278–290 (1988)
Kottke, D.P., Sun, Y.: Segmentation of coronary arteriograms by iterative ternary classification. IEEE Transaction on Biomedical Engineering 37, 778–785 (1990)
Francis, K., Quek, H., Kirbas, C.: Vessel extraction in medical images by wave-propagation and trace-back. IEEE Transaction on Medical Imaging 20 (2001)
Gudmundsson, M., El-Kwae, E.A., Kabuka, M.R.: Edge Detection in medical images using a genetic algorithm. IEEE Transaction on Medical Imaging 17, 469–474 (1998)
Osher, S., Sethian, J.A.: Fronts propagating with curvature dependent speed: Algorithms based on Hamilton Jacobi formulation. Journal of Computational Physics 79 (1988)
Yanagihara, Y., Sugahara, T., Sugimoto, N.: Extraction of vessel in brain using fast x-ray CT images. Systems and Computers in Japan 25, 78–85 (1994)
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© 2006 Springer-Verlag Berlin Heidelberg
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Köse, C. (2006). Fully Automatic Segmentation of Coronary Vessel Structures in Poor Quality X-Ray Angiogram Images. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_7
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DOI: https://doi.org/10.1007/11815921_7
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