146 [119] Re<strong>in</strong>bacher, C., Pock, T., Bauer, C., and Bisch<strong>of</strong>, H. (2010). Variational segmentation <strong>of</strong> elongated volumetric structures. In Computer Vision and Pattern Recognition. [120] Reit<strong>in</strong>ger, B., Bornik, A., Beichel, R., and Schmalstieg, D. (2006). Liver surgery plann<strong>in</strong>g us<strong>in</strong>g virtual reality. IEEE Comput. Graph. Appl., 26(6):36–47. [121] Sato, Y., Araki, T., Hanayama, M., Naito, H., and Tamura, S. (1998a). A viewpo<strong>in</strong>t determ<strong>in</strong>ation system for stenosis diagnosis and quantification <strong>in</strong> coronary angiographic image acquisition. IEEE Trans. Med. Imag<strong>in</strong>g, 17(1):121–137. [122] Sato, Y., Nakajima, S., Atsumi, H., Koller, T., Gerig, G., Yoshida, S., and Kik<strong>in</strong>is, R. (1997). <strong>3D</strong> multi-scale l<strong>in</strong>e filter for segmentation and visualization <strong>of</strong> curvil<strong>in</strong>ear structures <strong>in</strong> medical images. In CVRMed-MRCAS, pages 213–222. [123] Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., and Kik<strong>in</strong>is, R. (1998b). Three-dimensional multi-scale l<strong>in</strong>e filter for segmentation and visualization <strong>of</strong> curvil<strong>in</strong>ear structures <strong>in</strong> medical images. <strong>Medical</strong> Image Analysis, 2(2):143–168. [124] Schaap, M. et al. (2009a). Standardized evaluation methodology and reference database for evaluat<strong>in</strong>g coronary artery centerl<strong>in</strong>e extraction algorithms. <strong>Medical</strong> Image Analysis, 13:701–714. [125] Schaap, M., Mannies<strong>in</strong>g, R., Smal, I., van Walsum, T., van der Lugt, A., and Niessen., W. (2007a). Bayesian track<strong>in</strong>g <strong>of</strong> tubular structures and its application to carotid arteries <strong>in</strong> CTA. In Proc. Med. Image Comput. Assist. Interv., pages 562–570. [126] Schaap, M., Smal, I., Metz, C., van Walsum, T., and Niessen, W. (2007b). Bayesian track<strong>in</strong>g <strong>of</strong> elongated structures <strong>in</strong> 3d images. In Karssemeijer, N. and Lelieveldt, B., editors, Information Process<strong>in</strong>g <strong>in</strong> <strong>Medical</strong> Imag<strong>in</strong>g, 20th International Conference, IPMI 2007, Maastricht, NL, July 2-6, 2007, pages 74–85. [127] Schaap, M., Smal, I., Metz, C., van Walsum, T., and Niessen, W. (2009b). Coronary lumen segmentation us<strong>in</strong>g graph cuts and robust kernel regression. In Proc. <strong>of</strong> Information Process<strong>in</strong>g <strong>in</strong> <strong>Medical</strong> Imag<strong>in</strong>g, pages 528–539. [128] Schlathalter, T., Lorenz, C., Carlsen, I., Re<strong>in</strong>isch, S., and Deschamps, T. (2002). Simultaneous segmentation and tree reconstruction <strong>of</strong> the airways for virtual bronchoscopy. In Proc. <strong>of</strong> SPIE Med. Imag., pages 103–113.
BIBLIOGRAPHY 147 [129] Schmugge, S. J., Keller, S., Nguyen, N., Souvenir, R., Huynh, T., Clemens, M., and Sh<strong>in</strong>, M. C. (2008). <strong>Segmentation</strong> <strong>of</strong> vessels cluttered with cells us<strong>in</strong>g a physics based model. In Proc. <strong>of</strong> MICCAI, pages 127–134. [130] Selle, D., Preim, B., and Peitgen, H. (2002). Analysis <strong>of</strong> vasculature for liver surgical plann<strong>in</strong>g. IEEE Trans. Med. Imag., 21(11):1344–1357. [131] Shikata, H., H<strong>of</strong>fman, E., and Sonka, M. (2004). Automated segmentation <strong>of</strong> pulmonary vascular tree from <strong>3D</strong> CT images. In Proc. SPIE Med. Imag<strong>in</strong>g, pages 107–116. [132] Sluimer, I., Schilham, A., Prokop, M., and van G<strong>in</strong>neken, B. (2006). Computer analysis <strong>of</strong> computed tomography scans <strong>of</strong> the lung: A survey. IEEE Trans. Med. Imag., 25(4):385–405. [133] Soler, L., Del<strong>in</strong>gette, H., Malanda<strong>in</strong>, G., Montagnat, J., Ayache, N., Clement, J.- M., Koehl, C., Dourthe, O., Mutter, D., and Marescaux, J. (2000). A fully automatic anatomical, pathological and functional segmentation from ct-scans for hepatic surgery. In <strong>Medical</strong> Imag<strong>in</strong>g, SPIE proceed<strong>in</strong>gs, pages 246–255. [134] Soler, L., Del<strong>in</strong>gette, H., Malanda<strong>in</strong>, G., Montagnat, J., Ayache, N., Koehl, C., Dourthe, O., Malassagne, B., Smith, M., Mutter, D., and Marescaux, J. (2001). Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Computer Aided Surgery, 6(3):131–142. [135] Steger, C. (1998). An unbiased detector <strong>of</strong> curvel<strong>in</strong>ear structures. IEEE Trans. Pattern Anal. Mach. Intell., 20(2):113–125. [136] Swift, R. D., Kiraly, A. P., Sherbondy, A. J., Aust<strong>in</strong>, A. L., H<strong>of</strong>fman, E. A., McLennan, G., and Higg<strong>in</strong>s, W. E. (2002). Automatic axis generation for virtual bronchoscopic assessment <strong>of</strong> major airway obstructions. Comput. Med. Imag<strong>in</strong>g Graphics, 26(2):103– 118. [137] Szymczak, A. (2008). Vessel track<strong>in</strong>g by connect<strong>in</strong>g the dots. The Midas Journal. [138] Szymczak, A., Stillman, A., Tannenbaum, A., and Mischaikow, K. (2006). Coronary vessel trees from 3d imagery: A topological approach. <strong>Medical</strong> Image Analysis, 10(4):548–559. [139] Tang, C.-K., Medioni, G., and Lee, M.-S. (2001). N-dimensional tensor vot<strong>in</strong>g and application to epipolar geometry estimation. IEEE Trans. Pattern Anal. Mach. Intell., 23(8):829–844.
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Graz University of Technology Insti
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Abstract The segmentation of tubula
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Kurzfassung Die Segmentierung tubul
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Acknowledgements I am deeply gratef
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Contents 1 Introduction 1 1.0.1 Req
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CONTENTS xv 8 Conclusion and Outloo
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xviii LIST OF FIGURES 4.2 Shape pri
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List of Tables 3.1 Average centerli
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