Abstract
We present an efficient algorithm for vessel segmentation of Digital Subtraction Angiography (DSA) images. Continuous DSA images (projections), obtained by X-ray fluoroscopy with contrast-media, are normally used as road maps in vessel catheterization. A more efficient technique would consist in the use of a 3D model reconstruction of the vascular tree, instead of continuous X-ray scans, as a map. By separating vessel information from the undesired background (noise and signals coming from other organs and motion artefacts), efficient segmentation can play a key role in reducing the number of projections (X-ray scans) necessary to reconstruct a 3D vascular model. In what follows, the proposed method is described and some experimental results are reported, thus illustrating the behaviour of the algorithm when compared to other segmentation methods, ideated for the same application. The automatic calculation methods for the parameters used are also reported and discussed.
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References
Picano, E.: Informed consent and communication of risk from radiological and nuclear medicine examinations: how to escape from a communication inferno. BMJ 329, 849–851 (2004)
Placidi, G., Franchi, D., Marsili, L., Gallo, P.: Development of an auxiliary system for the execution of vascular catheter interventions with a reduced radiological risk; system description and first experimental results. Computer Method and Programs in Biomedicine 88, 144–151 (2007)
Placidi, G., Alecci, M., Sotgiu, A.: Theory of Adaptive Acquisition Method for Image Reconstruction from Projections and Application to EPR Imaging. J. of Magn. Reson. B 108, 50–57 (1995)
Placidi, G., Alecci, M., Sotgiu, A.: Angular Space-Domain Interpolation for Filtered Back Projection Applied to Regular and Adaptively Measured Projections. J. of Magn. Reson. B 110, 75–79 (1996)
Meijering, E.H.W., Niessen, W.J., Viegever, M.A.: Retrospective motion correction in digital subtraction angiography: a review. IEEE Transactions on Medical Imaging 18(1), 2–21 (1999)
Balter, S., Ergun, D., Tscholl, E., Buchmann, F., Verhoeven, L.: Digital subtraction angiography: fundamental noise characteristics. Radiology 152, 195–198 (1984)
Manniesing, R., Viergever, M.A., Niessen, W.J.: Vessel enhancing diffusion. A scale space representation of vessel structures. Medical Image Analysis 10, 815–825 (2006)
Liang, J., McInerney, T., Terzopoulos, D.: United Snakes. Medical Image Analysis 10, 215–233 (2006)
Mendonça, A.M., Campilho, A.: Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction. IEEE Transactions on Medical Imaging 25(9), 1200–1213 (2006)
Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and fluorescein retinal images. Medical Image Analysis 11, 47–61 (2007)
Passat, N., Ronse, C., Baruthio, J., Armspach, J.-P., Maillot, C.: Magnetic resonance angiography: From anatomical knowledge modelling to vessel segmentation. Medical Image Analysis 10, 259–274 (2006)
Manniesing, R., Velthuis, B.K., Van Leeuwen, M.S., Van Der Schaaf, I.C., Van Laar, P.J., Niessen, W.J.: Level set based cerebral vasculature segmentation and diameter quantification in CT angiography. Medical Image Analysis 10, 200–214 (2006)
Scherl, H., Hornegger, J., Prummer, M., Lell, M.: Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets. Medical Image Analysis 11, 21–34 (2007)
Soares, J.V.B., Leandro, R.J.J.G., Cesar-Jr., M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging 25(121), 1214–1222 (2006)
Chung, A.C.S.: Image Segmentation Methods for Detecting Blood Vessels in Angiography. In: Int. Conf. Control. Automation, Robotics and Vision, Singapore, pp. 1424–1429 (2006)
Sang, N., Peng, W., Li, H., Zhang, T.: Knowledge-based adaptive thresholding segmentation of digital subtraction angiography. Image and Vision Computing 25(8), 1263–1270 (2007)
Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)
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© 2008 Springer-Verlag Berlin Heidelberg
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Franchi, D., Gallo, P., Placidi, G. (2008). A Novel Segmentation Algorithm for Digital Subtraction Angiography Images: First Experimental Results. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_60
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DOI: https://doi.org/10.1007/978-3-540-89646-3_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89645-6
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