Материал: [2.1] 3D Imaging, Analysis and Applications-Springer-Verlag London (2012)

24.Ecabert, O., Peters, J., Schramm, H., Lorenz, C., von Berg, J., Walker, M.J., Vembar, M., Olszewski, M.E., Subramanyan, K., Lavi, G., Weese, J.: Automatic model-based segmentation of the heart in CT images. IEEE Trans. Med. Imaging 27, 1189–1201 (2008)

25.Edelsbrunner, H.: Geometry and Topology for Mesh Generation. Monographs on Applied and Computational Mathematics. Cambridge University Press, Cambridge (2001)

26.Epstein, C.L.: Introduction to the Mathematics of Medical Imaging, 2nd edn. SIAM, Philadelphia (2003)

27.Erasmus, L.J., Hurter, D., Naudé, M., Kritzinger, H.G., Acho, S.: A short overview of MRI artefacts. SA J. Radiol. 8(2), 13–17 (2004)

28.Fitzpatrick, M.J., West, J.B., Maurer, C.R. Jr.: Predicting error in rigid-body point-based registration. IEEE Trans. Med. Imaging 17(5), 694–702 (1998)

29.Fripp, J., Crozier, S., Warfield, S., Ourselin, S.: Automatic segmentation of the knee bones using 3D active shape models. In: Proceedings ICPR (2006)

30.Gardner, R.J.: In: Geometric Tomography. Cambridge University Press, Cambridge (2006)

31.Hajnal, J.V. (ed.): Medical Image Registration. CRC Press, Boca Raton (2001)

32.Hall, P., Ngan, M., Andreae, P.: Reconstruction of vascular networks using three-dimensional models. IEEE Trans. Med. Imaging 16(6), 919–929 (1997)

33.Han, X., Xu, C., Prince, J.L.: A topology preserving level set method for geometric deformable models. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 755–768 (2003)

34.Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13, 543–563 (2009)

35.Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Phys. Med. Biol. 46, R1–R45 (2001)

36.Hudson, H.M., Larkin, R.S.: Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans. Med. Imaging 13, 601–609 (1994)

37. Imai, K., Ikeda, M., Wada, S., Enchi, Y., Niimi, T.: Analysis of streak artefacts on CT images using statistics of extremes. Br. J. Radiol. 80(959), 911–918 (2007). http://bjr.birjournals.org/cgi/content/abstract/80/959/911. doi:10.1259/bjr/93741044

38.Johansen-Berg, H., Behrens, T.E. (eds.): Diffusion MRI from Quantitative Measurement to In-Vivo Neuroanatomy. Elsevier, Amsterdam (2009)

39.Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. SIAM, Philadelphia (2001). Unabridged edn.

40.Kass, M., Witkin, A., Terzopoulos, D.: Snakes—active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1987)

41.Klein, S., van der Heide, U.A., Lips, I.M., van Vulpen, M., Staring, M., Pluim, J.P.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med. Phys. 35, 1407–1417 (2008)

42.Kucera, D., Martin, R.W.: Segmentation of sequences of echocardiographic images using a simplified 3D active contour model with region-based external forces. Comput. Med. Imaging Graph. 21, 1–21 (1997)

43.Lerotic, M., Chung, A., Mylonas, G., Yang, G.Z.: Pq-space based non-photorealistic rendering for augmented reality. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. Lecture Notes in Computer Science, vol. 4792, pp. 102–109. Springer, Berlin (2007)

44.Liang, Z.P., Lauterbur, P.C.: Principles of Magnetic Resonance Imaging: a Signal Processing Perspective. Series on Biomedical Engineering. IEEE Press, New York (2000)

45.Liebling, M.: CT Reconstruction Demo Applet. Online (2001). http://bigwww.epfl.ch/demo/ jtomography/demo.html

46.Linguraru, M.G., Sandberg, J.K., Li, Z., Pura, J.A., Summers, R.M.: Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation. In: Proceedings MICCAI, pp. 1001–1008 (2009)

47.Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface reconstruction algorithm. Comput. Graph. 21(3), 163–169 (1987)

48.Lorenz, C., von Berg, J.: A comprehensive shape model of the heart. Med. Image Anal. 10, 657–670 (2006)

49.Lorenzo-Valdés, M., Sanchez-Ortiza, G.I., Elkington, A.G., Mohiaddin, R.H., Rueckert, D.: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med. Image Anal. 8, 255–265 (2004)

50.Malladi, R., Sethian, J., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)

51.Manniesing, R., Velthuis, B.K., can 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. Med. Image Anal. 10(2), 200–214 (2006)

52.Manzke, R., Meyer, C., Ecabert, O., Peters, J., Noordhoek, N.J., Thiagalingam, A., Reddy, V., Chan, R.C., Weese, J.: Automatic segmentation of rotational X-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures. IEEE Trans. Med. Imaging 29, 260–272 (2010)

53.McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey.

pean Conference on Computer Vision: Part IV, pp. 492–503. Springer, Berlin (2008). http://portal.acm.org/citation.cfm?id=1478237.1478274

55.Mei, L., Figl, M., Rueckert, D., Darzi, A., Edwards, P.: Sample sufficiency and number of modes to retain in statistical shape modelling. In: Metaxas, D., Axel, L., Fichtinger, G., Szkely, G. (eds.) Medical Image Computing and Computer-Assisted Intervention MICCAI 2008. Lecture Notes in Computer Science, vol. 5241, pp. 425–433. Springer, Berlin (2008)

56.Muralidhar, G.S., Bovik, A.C., Giese, J.D., Sampat, M.P., Whitman, G.J., Haygood, T.M., Stephens, T.W., Markey, M.K.: Snakules: a model-based active contour algorithm for the annotation of spicules on mammography. IEEE Trans. Med. Imaging 29, 1768–1780 (2010)

57.Natterer, F., Wübbeling, F.: Mathematical Methods in Image Reconstruction. Monographs on Mathematical Modeling and Computation. SIAM, Philadelphia (2001)

58.Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8), 987–1010 (2006)

59.Olabarriaga, S.D., Smeulders, A.W.M.: Interaction in the segmentation of medical images: a survey. Med. Image Anal. 5(2), 127–142 (2001)

60.Paragios, N.: A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans. Med. Imaging 22(6), 773–776 (2003)

61.Pelizzari, C.A., Tan, K.K., Levin, D.N., Chen, G.T.Y., Balter, J.: Interactive 3D patient-image registration. In: Colchester, A.C.F., Hawkes, D.J. (eds.) Information Processing in Medical Imaging, pp. 133–141. Springer, Berlin (1991)

62.Peters, J., Ecabert, O., Meyer, C., Schramm, H., Kneser, R., Groth, A., Weese, J.: Automatic whole heart segmentation in static magnetic resonance image volumes. In: Proceedings MICCAI, pp. 402–410 (2007)

63.Peters, T.M., Cleary, K.R.: Image-Guided Interventions: Technology and Applications. Springer, Berlin (2008)

64.Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 315–338 (2000)

65.Pratt, P., Mayer, E., Vale, J., Cohen, D., Edwards, E., Darzi, A., Yang, G.Z.: An effective visualisation and registration system for image-guided robotic partial nephrectomy. J. Robotic Surgery (2012)

66.Rajan, J., Poot, D., Juntu, J., Sijbers, J.: Noise measurement from magnitude MRI using local estimates of variance and skewness. Phys. Med. Biol. 55(16), N441-9 (2010). http://www.ncbi.nlm.nih.gov/pubmed/20679694. doi:10.1088/0031-9155/55/16/N02

67.Rohlfing, T., Brandt, R., Maurer, C. Jr., Menzel, R.: Bee brains, b-splines and computational democracy: generating an average shape atlas. In: Proceedings MMBIA, pp. 187–194 (2001)

68.Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.R. Jr.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21, 1428–1442 (2004)

69.Roth, H., McClelland, J., Modat, M., Boone, D., Hu, M., Ourselin, S., Slabaugh, G.G., Halligan, S., Hawkes, D.J.: Establishing spatial correspondence between the inner colon surfaces from prone and supine ct colonography. In: Proceedings MICCAI (Beijing), pp. 497–504 (2010)

70.Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3D statistical deformation models of the brain using nonrigid registration. IEEE Trans. Med. Imaging 22, 1014–1025 (2003)

71.Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)

72.Rhode, K., Sermesant, M.: Modeling and registration for electrophysiology procedures based on three-dimensional imaging. Curr. Cardiovasc. Imaging Rep. 4, 116–126 (2011)

73.Shepp, L., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imaging 1, 113–122 (1982)

74.Slepian, D. (ed.): Key Papers in the Development of Information Theory. Reprint Series. IEEE Press, New York (1974)

75.Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit. 32(1), 71–86 (1999)

76.Suetens, P.: Fundamentals of Medical Imaging. Cambridge University Press, Cambridge (2002)

77.Tabakov, S., Smith, P.H.S., Milano, F., Strand, S.E., Lewis, C. (eds.): Encyclopedia of Medical Physics. Taylor & Francis, London (2012). ISBN 9781439846520 http://www.emitel2.eu/. available [Online]

78.Tao, X., Prince, J.L., Davatzikos, C.: Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain. IEEE Trans. Med. Imaging 21, 513–524 (2002)

79.Viola, P.A., Wells, W.M.: Alignment by maximisation of mutual information. In: Proceedings of the 5th International Conference on Computer Vision, pp. 15–23 (1995)

80.Weickert, J., Laidlaw, D. (eds.): Visualization and Processing of Tensor Fields. II. Mathematics+Visualization. Springer, Berlin (2009)

81.Weickert, J., Hagen, H. (eds.): Visualization and Processing of Tensor Fields. Mathematics+Visualization. Springer, Berlin (2006)

82.Williams, D.J., Shah, M.: A fast algorithm for active contours and curvature estimation. CVGIP, Image Underst. 55, 14–26 (1992)

83.Xu, C., Pham, D.L., Rettmann, M.E., Yu, D.N., Prince, J.L.: Reconstruction of the human cerebral cortex from magnetic resonance images. IEEE Trans. Med. Imaging 18, 467–480 (1999)

84.Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: Userguided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)

85.Zhu, X.J., Zhang, P.F., Shao, J.H., Cheng, Y.Z., Zhang, Y., Bai, J.: A snake-based method for segmentation of intravascular ultrasound images and its in vivo validation. Ultrasonics 51, 181–189 (2011)

86.Zhuang, X., Rhode, K.S., Razavi, R.S., Hawkes, D.J., Ourselin, S.: A registration-based propagation framework for automatic whole heart segmentation of cardiac MRI. IEEE Trans. Med. Imaging 29, 1612–1625 (2010)