Bibliography

[1] Gerig, G., Martin, J., Kikinis, R., Kübler, O., Shenton, M., and Jolesz, F., Automatic segmentation of düal-echo MR head data, In: Proceedings of Information Processing in Medical Imaging'91, Wye, GB, 1991, pp.175-187.

[2] Düda, R. and Hart, P., Pattern Classification and Scene Analysis, Wiley, New York, 1973.

[3] Shattück, D., Sandor-Leahy, S., Schaper, K., Rottenberg, D., and Leahy, R., Magnetic resonance image tissue classification using a partial volume model, Neuroimage, Vol. 13, pp. 856-876, 2001.

[4] S. Rüan, J. X., C. Jaggi and Bloyet, J., Brain tissue classification of magnetic resonance images using partial volume modeling, IEEE Trans. Med. Imaging, Vol. 19, No. 12, pp. 172-186, 2000.

[5] Gerig, G., Kübler, O., Kikinis, R., and Jolesz, F., Nonlinear anisotropic filtering of MRI data, IEEE Trans. Med. Imaging, Vol. 11, No. 2, pp. 221232, 1992.

[6] Güillemaüd, R. and Brady, M., Estimating the bias field of MR images, IEEE Trans. Med. Imaging, Vol. 16, No. 3, pp. 238-251, 1997.

[7] M. Styner, G. S., Ch. Brechbühler and Gerig, G., Parametric estimate of intensity inhomogeneities applied to MRI, IEEE Trans. Med. Imaging, Vol. 19, No. 3, pp. 153-165, 2000.

[8] Wells, W., Grimson, W., Kikinis, R., and Jolesz, F., Adaptive segmentation of MRI data, IEEE Trans. Med. Imaging, Vol. 15, No. 4, pp. 429-443,1996.

[9] Van Leempüt, K., Maes, F., Bello, F., Vandermeülen, D., Colchester, A., and Süetens, P., Automated segmentation of MS lesions from multichannel MR images, In: Proceedings of Second International Conference on Medical Image Computing and Computer-Assisted Interventions, MICCAI'99, Taylor, C. and Colchester, A., eds., Lecture Notes in Computer Science, Vol. 1679, Springer-Verlag, New-York, pp. 11-21, 1999.

[10] Li, S. Z., Markov Random Field Modeling in Computer Vision, SpringerVerlag, Tokyo, 1995.

[11] Serra, J., Image Analysis and Mathematical Morphology, Academic Press, San Diego, 1982.

[12] Raya, S., Low-level segmentation of 3-D magnetic resonance brain images—A rule-based system, IEEE Trans. Med. Imaging, Vol. 9, No. 3, pp. 327-337, 1990.

[13] Stansfield, S., ANGY: A rule-based system for automatic segmentation of coronary vessels from digital subtracted angiograms, IEEE Trans. Patt. Anal. Mach. Intell., Vol. 8, No. 2, pp. 188-199, 1986.

[14] Bajcsy, R. and Kovacic, S., Multiresolution elastic matching, Comput. Vision Graph. Image Process., Vol. 46, pp. 1-21, 1989.

[15] Evans, A. C., Collins, D. L., and Holmes, C. J., Toward a probabilistic atlas of human neuroanatomy, In: Brain Mapping: The Methods, Mazziotta, J. C. and Toga, A. W., eds., Academic Press ISBN 0126930198 pp. 343361, 1996.

[16] Jiang, H., Holton, K., and Robb, R., Image registration of multimodal-ity 3-D medical images by chamfer matching, In: Proceedings of Biomedical. Image Processing and 3D Microscopy, SPIE, Vol. 1660, SPIE, The International Society of Optical Engineering pp. 356-366, 1992.

[17] Christensen, G., Miller, M., and Vannier, M., Individualizing neu-roanatomical atlases using a massively parallel computer, IEEE Computer, pp. 32-38, January 1996.

[18] Bookstein, F., Shape and the information in medical images: A decade of the morphometric synthesis, Comput. Vision. Image Understand., Vol. 66, No. 2, pp. 97-118, 1997.

[19] Evans, A., Kamber, M., Collins, D., and MacDonald, D., An MRI-based probabilistic atlas of neuroanatomy, In: Magnetic Resonance Scanning and Epilepsy, Shorvon, S., ed., Plenum Press, New York, pp. 263-274, 1994.

[20] Wang, Y. and Staib, L., Elastic model based non-rigid registration incorporating statistical shape information, In: Proc. First Int. Conf. on Medical Image Comp. and Comp. Assisted Interventions, Vol. 1679 of Lecture Notes in Comp. Sci., pp. 1162-1173, Springer-Verlag, New York, 1998.

[21] Terzopoulos, D. and Metaxas, D., Dynamic 3D models with local and global deformations: Deformable superquadrics, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 13, No. 7, pp. 703-714, 1991.

[22] Vemuri, B. and Radisavljevic, A., Multiresolution stochastic hybrid shape models with fractal priors, ACM Trans. Graphics, Vol. 13, No. 2, pp. 177-200, 1994.

[23] Staib, L. and Duncan, J., Boundary finding with parametrically deformable models, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 14, No. 11, pp. 1061-1075, 1992.

[24] Brechbühler, C., Gerig, G., and Kübler, O., Parametrization of closed surfaces for 3-D shape description, CVGIP: Image Understand., Vol. 61, pp. 154-170, 1995.

[25] Cootes, T., Cooper, D., Taylor, C., and Graham, J., Training models of shape from sets of examples, In: Proceedings of The British Machine Vision Conference (BMVC) Springer-Verlag, New-York, pp. 9-18, 1992.

[26] Cootes, T. and Taylor, C., Active shape models—'Smart snakes,' In: Proceedings of The British Machine Vision Conference (BMVC) SpringerVerlag, New-York, pp. 266-275, 1992.

[27] Rangarajan, A., Chui, H., and Bookstein, F., The softassign procrustes matching algorithm, information processing in medical imaging, pp. 29-42, 1997. Available at http://noodle.med.yale.edu/anand/ps/ ipsprfnl.ps.gz.

[28] Tagare, H., Non-rigid curve correspondence for estimating heart motion, Inform. Process. Med. Imaging, Vol. 1230, pp. 489-494, 1997.

[29] Kotcheff, A. and Taylor, C., Automatic construction of eigenshape models by genetic algorithm, Inform. Process. Med. Imaging, Vol. 1230, pp. 1-14, 1997.

[30] Kelemen, A., Szekely, G., and Gerig, G., Elastic model-based segmentation of 3-d neuroradiological data sets, IEEE Trans. Med. Imaging, Vol. 18, pp. 828-839, 1999.

[31] Staib, L. and Duncan, J., Model-based deformable surface finding for medical images, IEEE Trans. Med. Imaging, Vol. 15, No. 5, pp. 1-12, 1996.

[32] Cootes, T. F., Taylor, C. J., Cooper, D. H., and Graham, J., Active shape models—Their training and application, Comput. Vision Image Understand., Vol. 61, No. 1, pp. 38-59, 1995.

[33] Szekely, G., Kelemen, A., Brechbuhler, C., and Gerig, G., Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models, Med. Image Anal., Vol. 1, No. 1, pp. 19-34, 1996.

[34] McInerney, T. andTerzopoulos, D., Deformable models in medical image analysis: A survey, Med. Image Anal., Vol. 1, No. 2, pp. 91-108, 1996.

[35] Cootes, T., Edwards, G., and Taylor, C., Active appearance models, In: Proceedings of the European Conference on Computer Vision, Vol. 2, Springer-Verlag, New-York, pp. 484-498, 1998.

[36] Kelemen, A., Szekely, G., and Gerig, G., Elastic model-based segmentation of 3-d neuroradiological data sets, IEEE Trans. Med. Imaging, Vol. 18, No. 10, pp. 828-839, 1999.

[37] Kruggel, F. and Lohmann, G., Automatical adaption of the stereotactical coordinate system in brain MRI datasets, In: Information Processing in Medical Imaging, Springer-Verlag, New York, pp. 471-476, 1997.

[38] Barrett, W. and Mortensen, E., Interactive live-wire boundary extraction, Medical Image Analysis, pp. 331-341, 1997. Available at citeseer.nj.nec.com/barrett97interactive.html.

[39] Fischler, M., Tenenbaum, J., and Wolf, H., Detection of roads and linear structures in low-reslution aerial imagery using a multisource knowledge integration technique, Comput. Graph. Image Process., Vol. 15, pp. 201-233, 1981.

[40] O'Donnell, L., Weslin, C.-T., Grimson, W. E. L., Ruiz-alzola, J., Shenton, M. E., and Kikinis, R., Phase-based user-steered image segmentation, In: International Conference on Medical Image Computing and ComputerAssisted Intervention (MICCAI), 2001, pp. 1022-1030.

[41] Falcao, A. and Udapa, J., A 3D generalization of user-steered live-wire segmentation, Med. Image Anal., Vol. 4, No. 1, pp. 389-402, 1997.

[42] Falcao, A., Udapa, J., and Miyazawa, F., An ultra-fast user-steered image segmentation paradigm: Live wire on the fly, IEEE Trans. Med. Imaging, Vol. 19, No. 1, pp. 55-62, 2000.

[43] Haenselmann, T. and Effelsberg, W., Wavelet-based semi-automatic live-wire segmentation, SPIE Human Vision and Electronic Imaging VIII, Vol. 5007, pp. 260-269, 2003. Available at citeseer.nj.nec.com/569760.html.

[44] Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models, Int. J. Comput. Vision, Vol. 1, No. 4, pp. 321-331, 1988.

[45] Canny, J., A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, No. 6, pp. 679-698, 1986.

[46] Fua, P. and Leclerc, Y., Model driven edge detection, Mach. Vision Appl.,

[47] Leymarie, F. and Levine, M., Tracking deformable objects in the plane using an active contour model, IEEE Trans. Pattern Anal. Mach. Intell.,

[48] Samadani, R., Changes in connectivity in active contour models, In: Proceedings of the IEEE Workshop on Visual Motion, Irvine, California, March 1989, pp. 337-343.

[49] Terzopoulos, D., On matching deformable models to images, Topical Meeting Mach. Vision Tech. Digest Series, Vol. 12, pp. 160-167, 1987.

[50] Cohen, L. and Cohen, I., A finite element method applied to new active contour models and 3D reconstructions, In: Proceedings of the Third International Conference on Computer Vision, Osaka, Japan, Dec. 1990, pp. 587-591.

[51] Cohen, I., Cohen, L. D., and Ayache, N., Using deformable surfaces to segment 3-D images and infer differential structures, Comput. Vision Graph. Image Process., Vol. 56, No. 2, pp. 242-263, 1992.

[52] Hug, J., Brechbtihler, C., and Szekely, G., Tamed snake: Aparticle system for robust semi-automatic segmentation, In: MICCAI, 1999, pp. 106-115.

[53] Dyn, N., Levin, D., and Gregory, J., A 4-point interpolatory subdivision scheme for curve design, Comput. Aided Geomet. Design, Vol. 4, No. 4, pp. 257-268, 1987.

[54] Hug, J., Semi-Automatic Segmentation of Medical Imagery, Ph.D. Thesis, ETH Zurich-Swiss Federal Institute of Technology, 2001.

[55] Dyn, N., Levin, D., and Gregory, J., A butterfly subdivision scheme for surface interpolation with tension control, Trans. Graph., Vol. 9, No. 2, pp. 160-169, 1990.

[56] Zorin, D., Schroder, P., and Sweldens, W., Interpolating subdivision for meshes of arbitrary topology, In: SIGGRAPH, August 1996, pp. 189-192.

[57] Kobbelt, L., Iterative Erzeugung glatter Interpolatoren., Ph.D. Thesis, University at Karlsruhe, 1994.

[58] Schneider, R. and Kobbelt, L., Geometric fairing of irregular meshes for free-form surface design, Comput. Aided Geomet. Design, Vol. 18, No. 4, pp. 359-379, 5 2001.

[59] Desbrun, M., Meyer, M., Schroder, P., and Barr, A., Discrete Differential-Geometry Operators in nD, preprint, The Caltech Multi-Res Modeling Group, 2000.

[60] Neuenschwander, W., Fua, P., Szekely, G., and Kubler, O., Initializing snakes, In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1994, pp. 658-663.

[61] Hug, J., Brechbuhler, C., and Szekely, G., Model-based initialisation for segmentation, In: Proceedings of 6th European Conference on Computer Vision (ECCV 2000), Part II, Vernon, D., ed., Lecture Notes in Computer Science, Springer, Berlin pp. 290-306, 2000.

[62] Harders, M. and Szekely, G., Enhancing human computer interaction in medical segmentation, Proc. IEEE, Vol. 91, No. 9, pp. 1430-1442, 2003.

[63] Rosenberg, L., Virtual fixtures: Perceptual tools for telerobotic manipulation, In: IEEE Virtual Reality Annual International Symposium, 1993, pp. 76-82.

[64] Sayers, C. and Paul, R., An operator interface for teleprogramming employing synthetic fixtures, Presence Teleoperat. Virtual Environ., Vol. 3, pp. 309-320, 1994.

[65] Harders, M. and Szekely, G., New paradigms for interactive 3D volume segmentation, J. Visual. Comput. Animation, Vol. 13, pp. 85-95, 2002.

[66] Karabassi, E.-A., Papaioannou, G., andTheoharis, T., Afastdepth-buffer-based voxelization algorithm, J. Graph. Tools, Vol. 4, No. 4, pp. 5-10, 1999.

0 0

Post a comment