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Erschienen in:
Object Segmentation and Markov Random Fields Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Atlas-based Segmentation

verfasst von : M. Bach Cuadra, V. Duay, J.-Ph. Thiran

Erschienen in: Handbook of Biomedical Imaging

Verlag: Springer US

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Abstract

Image segmentation is a main task in many medical applications such as surgical or radiation therapy planning, automatic labelling of anatomical structures or morphological and morphometrical studies. Segmentation in medical imaging is however challenging because of problems linked to low contrast images, fuzzy object-contours, similar intensities with adjacent objects of interest, etc. Using prior knowledge can help in the segmentation task. A widely used method consists to extract this prior knowledge from a reference image often called atlas. We review in this chapter the existing approaches for atlas-based segmentation in medical imaging and we focus on those based on a volume registration method. We present the problem of using atlas information for pathological image analysis and we propose our solution for atlas-based segmentation in MR image of the brain when large space-occupying lesions are present. Finally, we present the new research directions that aim at overcome current limitations of atlas-based segmentation approaches based on registration only.

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Vorheriges Kapitel Image-based haemodynamics simulation in intracranial aneurysms
Nächstes Kapitel Integration of Topological Constraints in Medical Image Segmentation
Fußnoten
1
Objects of interest can be for instance points, lines, surfaces or volumes.
 
2
Note that the vector field points the origin, and not the destiny, of a voxel.
 
Literatur
1.
J. An, Y. Chen, F. Huang, D. Wilson, and E. Geiser. A variational pde based level set method for a simultaneous segmentation and non-rigid registration. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 286–293, 2005.
2.
E. Angelini, Y. Jin, and A. Laine. Handbook of Biomedical Image Analysis, chapter State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities, pages 47–101. Springer US, 2007.
3.
M. Bach Cuadra. Atlas-based segmentation and classification of magnetic resonance brain images. THSE NO 2875, École Polytechnique Fédérale De Lausanne, 2003.
4.
M. Bach Cuadra, L. Cammoun, T. Butz, O. Cuisenaire, and J. Thiran. Comparison and validation of tissue modelization and statistical classification methods in t1-weighted mr brain images. IEEE Transactions on Medical Imaging, 24(12):1548– 1565, 2005.CrossRef
5.
M. Bach Cuadra, O. Cuisenaire, R. Meuli, and J.-P. Thiran. Automatic segmentation of internal structures of the brain in mri using a tandem of affine and non-rigid registration of an anatomical atlas. In International Conference in Image Processing (ICIP), October 2001.
6.
M. Bach Cuadra, M. De Craene, V. Duay, B. Macq, C. Pollo, and J. Thiran. Dense deformation field estimation for atlas-based segmentation of pathological mr brain images. Methods and Programs in Biomedicine, 84(2-3):66–75, 2006.CrossRef
7.
M. Bach Cuadra, C. Polio, A. Bardera, O. Cuisenaire, J.-G. Villemure, and J. Thiran. Atlas-based segmentation of pathological mr brain images using a model of lesion growth. IEEE Trans. Med. Imag., 23(10):1301–1314, 2004.CrossRef
8.
C. Baillard, P. Hellier, and B. C. Cooperation between level set techniques and 3d registration for the segmentation of brain structures. In International Conference on Pattern Recognition (ICPR), pages 991–994, 2000.
9.
R. Bajcsy. Digital anatomy atlas and its registration to mri, fmri,pet: The past presents a future. In Biomedical Image Registration, Second International Workshop (WBIR), pages 201–211, Philadelphia, USA, 2003.
10.
R. Bajcsy and S. Kovacic. Multi resolution elastic matching. Computer Vision, Graphics and Image Processing, 46:1–21, 1989.
11.
R. Bajcsy, R. Lieberson, and M. Reivich. A computerized system for the elastic matching of deformed radiographic images to idelaized atlas images. Journal of Computer Assisted Tomography., 7(4):618–625, 1983.CrossRef
12.
K. K. Bhatia, J. V. Hajnal, B. K. Puri, A. Edwards, and D. Rueckert. Consistent groupwise non-rigid registration for atlas construction. In IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro., pages 908–911, Arlington, USA, 2004.
13.
P.-Y. Bondiau, G. Malandain, S. Chanalet, P. Marcy, J.-L. Habrand, F. Fauchon, P. Paquis, A. Courdi, O. Commowick, I. Rutten, and N. Ayache. Atlas-based automatic segmentation of mr images: validation study on the brainstem in radiotherapy context. Int J Radiat Oncol Biol Phys., 61(1):289–298, 2005.CrossRef
14.
M. Bro-Nielsen and C. Gramkow. Fast fluid registration of medical images. In Visualization in Biomedical Computing (VBC ’96), pages 267–276, 1996.
15.
T. Brox, A. Bruhn, N. Papenberb, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. In 8th European Conf. Computer Vision, Part IV: Lecture Notes in Computer Science, volume 3024, pages 25–36, 2004.
16.
M. Cabezas, A. Oliver, X. Lladó, J. Freixenet, and M. Bach Cuadra. A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine, 104(3):e158–e177, 2011.CrossRef
17.
Y. Chen, F. Huang, R. Tagare, H. D. amd Murali, D. Wilson, and E. A. Geiser. Using prior shape and intensity profile in medical image segmentation. In IEEE International Conference on Computer Vision, pages 1117–1124, 2003.
18.
G. E. Christensen, R. D. Rabbitt, and M. I. Miller. 3d brain mapping using a deformable neuroanatomy. Phys. Med. Biol., 39:609–618, 1994.CrossRef
19.
C. Ciofolo. Atlas-based segmentation using level sets and fuzzy labels. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 310–317, 2004.
20.
A. Collignon, D. Vandermeulen, P. Suetens, and G. Marchal. 3d multi-modality medical image registration using feature space clustering. In Computer Vision, Virtual Reality, and Robotics in Medicine, volume 905, pages 195–204, 1995.
21.
22.
L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans. Automatic 3-d model-based neuroanatomical segmentation. Human Brain Mapping, 3(3):190–208, 1995.CrossRef
23.
O. Commowick and G. Malandain. Evaluation of atlas construction strategies in the context of radiotherapy planning. In Proceedings of the SA2PM Workshop (From Statistical Atlases to Personalized Models), Copenhagen, October 2006. Held in conjunction with MICCAI 2006.
24.
O. Commowick, R. Stefanescu, P. Fillard, V. Arsigny, N. Ayache, X. Pennec, and G. Malandain. Incorporating statistical measures of anatomical variability in atlas-to-subject registration for conformal brain radiotherapy. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 2, pages 927–934, 2005.
25.
D. Cooper, C. Cootes, T.F. and Taylor, and J. Graham. Active shape models - their training and application. Computer Vision and Image Understanding, 2(61):38–59, 1995.
26.
T. Cootes, C. Beeston, and C. Edwards, G.J.and Taylor. A unified framework for atlas matching using active appearance models. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2:927–934, 2005.
27.
O. Cuisenaire, J.-P. Thiran, B. Macq, C. Michel, A. De Volder, and F. Marques. Automatic registration of 3d mr images with a computerized brain atlas. In SPIE Medical Imaging, volume 1719, pages 438–449, 1996.
28.
B. Dawant, S. Hartmann, and S. Gadamsetty. Brain Atlas Deformation in the Presence of Large Space-occupying Tumors. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)., pages 589–596, 1999.
29.
B. Dawant, S. Hartmann, J.-P. Thirion, F. Maes, D. Vandermeulen, and P. Demaerel. Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, methodology and validation on normal subjects. IEEE Transactions on Medical Imaging, 18(10):902–916, 1999.CrossRef
30.
B. M. Dawant, R. Li, E. Cetinkaya, C. Kao, J. M. Fitzpatrick, and P. E. Konrad. Computerized atlas-guided positioning of deep brain stimulators: A feasibility study. WBIR, pages 142–150, 2003.
31.
M. De Craene, A. du Bois d’Aische, B. Macq, and S. K. Warfield. Multi-subject registration for unbiased statistical atlas construction. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)., pages 655–662, 2004.
32.
P.-F. D’Haese. Automatic segmentation of brain structures for radiation therapy planning. In SPIE Medical Image Processing, pages 517–526, 2003.
33.
M. Droske, W. Ring, and M. Rumpf. Mumford-shah based registration. Computing and Visualization in Science (CVS), 2007. to appear in CVS.
34.
V. Duay, M. Bach Cuadra, X. Bresson, and J.-P. Thiran. Dense deformation field estimation for atlas registration using the active contour framework. In European Signal Processing Conference (EUSIPCO), 2006.
35.
V. Duay, X. Bresson, N. Houhou, M. Bach Cuadra, and J.-P. Thiran. Registration of multiple regions derived from the optical flow model and the active contour framework. In European Signal Processing Conference (EUSIPCO), 2007.
36.
V. Duay, P. DHaese, R. Li, and B. Dawant. Non-rigid registration algorithm with spatially varying stiffness properties. In IEEE International Symposium on Biomedical Imaging (ISBI), pages 408–411, 2004.
37.
V. Duay, N. Houhou, and J.-P. Thiran. Atlas-based segmentation of medical images locally constrained by level sets. In International Conference in Image Processing (ICIP), 2005.
38.
M. Esiri and M. J. The neuropathology of dementia. Cambridge University Press, 2002.
39.
A. Evans, D. Collins, P. Neelin, M. Kamber, and T. S. Marrett. Three-dimensional correlative imaging: applications in human brain mapping. Functional Imaging: Technical Foundations, pages 145–162, 1994.
40.
M. Ferrant, A. Nabavi, B. Macq, P. M. Black, F. A. Jolesz, R. Kikinis, and S. K. Warfield. Serial registration of intraoperative mr images of the brain. Medical Image Analysis, 6(4): 337–359, 2002.CrossRef
41.
42.
R. Galloway, R. Macuinas, W. Bass, and W. Carpini. Optical localization for interactive image-guided neurosurgery. Medical Imaging, 2164:137–145, 1994.
43.
J. Gee, M. Reivich, and R. Bajcsy. Elastically deforming a three-dimensional atlas to match anatomical brain images. J. Comput. Assist. Tomogr., 17:225–236, 1993.CrossRef
44.
S. Gorthi, V. Duay, X. Bresson, M. Bach Cuadra, F. J. Sánchez Castro, C. Pollo, A. S. Allal, and J. P. Thiran. Active deformation fields: dense deformation field estimation for atlas-based segmentation using the active contour framework. Medical Image Analysis, 15(6):787–800, 2011.CrossRef
45.
S. Gorthi, V. Duay, N. Houhou, M. Bach Cuadra, U. Schick, M. Becker, A. Allal, and J.-P. Thiran. Segmentation of head and neck lymph node regions for radiotherapy planning, using active contour based atlas registration. IEEE Journal of selected topics in signal processing, 3(1):135–147, 2009.CrossRef
46.
T. Greitz, C. Bohm, S. Holte, and L. Eriksson. A computerized brain atlas: construction, anatomical content and some applications. Journal of Computer Assisted Tomography, 15(1):26–38, 1991.CrossRef
47.
A. Guimond, J. Meunier, and J. Thirion. Average brain models: a convergence study. Comput. Vis. Image Underst., 77(9):192–210, 2000.CrossRef
48.
P. Haese, V. Duay, R. Li, A. du Bois Aische, A. Cmelak, E. Donnelly, K. Niermann, T. Merchant, B. Macq, and B. Dawant. Automatic segmentation of brain structures for radiation therapy planning. Medical Imaging Conference SPIE, 2003.
49.
J. Haller, A. Banerjee, G. Christensen, M. Gado, S. Joshi, M. Miller, Y. Sheline, M. Vannier, and J. Csernansky. 3d hippocampal morphometry by high dimensional transformation of a neuroanatomical atlas. Radiology, 202(2):504–510, 1997.CrossRef
50.
P. Hellier, C. Barillot, I. Corouge, B. Gibaud, G. Le Goualher, D. Collins, A. Evans, G. Malandain, and N. Ayache. Retrospective evaluation of inter-subject brain registration. IEEE Transactions on Medical Imaging, 22(9):1120–1130, 2003.CrossRef
51.
K. Hohne, M. Bomans, M. Riemer, R. Schubert, U. Tiede, and W. Lierse. A volume based anatomical atlas. IEEE Computer Graphics and Applications., 12(4):72–78, 1992.CrossRef
52.
N. Houhou, V. Duay, A. S. Allal, and J.-P. Thiran. Medical images registration with a hierarchical atlas. In EUSIPCO, 2005.
53.
D. V. Iosifescu, M. E. Shenton, S. K. Warfield, R. Kikinis, J. Dengler, F. A. Jolesz, and R. W. Mccarley. An automated registration algorithm for measuring mri subcortical brain structures. Neuroimage, 6(1):13–25, July 1997.CrossRef
54.
S. Joshi, B. Davis, M. Jomier, and G. Gerig. Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage., 23(1):151–160, 2004.CrossRef
55.
T. Kapur, P. A. Beardsley, S. F. Gibson, W. E. L. Grimson, and W. M. Wells. Model based segmentation of clinical knee mri. In Proc. IEEE Int’l Workshop on Model-Based 3D Image Analysis, pages 97–106, 1998.
56.
M. Kass, A. Witkin, and T. D. Snakes: active contour models. In First international conference on computer vision, pages 259–268, 1987.
57.
M. Kaus, S. Warfield, A. Nabavi, E. Chatzidakis, P. Black, F. Jolesz, and R. Kikinis. Segmentation of meningiomas and low grade gliomas in mri. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 1–10, 1999.
58.
R. Kikinis, M. Shenton, D. Iosifescu, R. McCarley, P. Saiviroonporn, H. Hokama, A. Robatino, D. Metcalf, C. Wible, C. Portas, R. Donnino, and F. Jolesz. A digital brain atlas for surgical planning, model driven segmentation and teaching. IEEE Transactions on Visualization and Computer Graphics., 2(3):232–241, 1996.CrossRef
59.
S. Kyriacou and C. Davatzikos. Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model. IEEE Trans. Med. Imaging, 18(7):580–592, 1999.CrossRef
60.
61.
K. V. Leemput, F. Maes, D. Vandermeulen, and P. Suetens. Automated model-based bias field correction of mr images of the brain. IEEE Transactions on Medical Imaging, 18(10): 897–908, 1999.CrossRef
62.
T. Liu, D. Shen, and C. Davatzikos. Deformable registration of tumor-diseased brain images. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 720–728, 2004.
63.
P. Lorenzen, B. Davis, and S. Joshi. Unbiased atlas formation via large deformations metric mapping. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)., volume 2, pages 411–418, Palm Springs, California, USA, 2005.
64.
D. Louis Collins, G. Le Goualher, and A. Evans. Non-linear cerebral registration with sulcal constraints. Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 974–984, 1998.
65.
F. Maes and A. Collignon. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16, 1997.
66.
C. R. Maurer and J. M. Fitzpatrick. Interactive ImageGuided Neurosurgery, chapter A review of medical image registration, pages 17–44. American Association of neurological surgeons, 1993.
67.
J. C. Mazziotta, A. W. Toga, and R. S. J. Frackowiak. Brain Mapping: The Disorders. Academic Press, 2000.
68.
69.
M. Miga, T. Sinha, D. Cash, R. Galloway, and R. Weil. Cortical surface registration for image-guided neurosurgery using laser range scanning. IEEE Transactions on Medical Imaging, 22(8):973–985, 2003.CrossRef
70.
M. Moelich and T. Chan. Joint segmentation and registration using logic models. Technical Report 03-06, Mathematics Department, UCLA, 2003.
71.
A. Mohamed and C. Davatzikos. Finite element modeling of brain tumor mass-effect from 3d medical images. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)., pages 400–408, 2005.
72.
73.
A. Noe, S. Kovacic, and J. Gee. Segmentation of cerebral mri scans using a partial volume model, shading correction, and an anatomical prior. In SPIE Medical Image Processing, 2001.
74.
W. L. Nowinski and D. Belov. Toward atlas-assisted automatic interpretation of mri morphological brain scans in the presence of tumor. Academic Radiology, 12(8):1049–1057, August 2005.CrossRef
75.
S. Osher and N. Paragios. Geometric Level Set Methods in Imaging Vision and Graphics, chapter Shape analysis twoards model-based segmentation, pages 231–250. Springer Verlag, New York, 2003.
76.
S. Osher and J. A. Sethian. Fronts propagating with curvature-dependent speed - algorithms based on hamilton-jacobi formulations. Journal of Computational Physics, 79(1):12–49, 1988.CrossRefMATHMathSciNet
77.
N. Pal and S. Pal. A review on image segmentation techniques. Pattern Recognition, 26(9):1277–1294, 1993.CrossRef
78.
N. Paragios. A variational approach for the segmentation of the left ventricle in mr cardiac images. In Proceedings of IEEE Workshop on Variational and Level Set Methods in Computer Vision, pages 153–160, 2001.
79.
N. Paragios. A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Transactions on Medical Imaging, 22:773–776, 2003.CrossRef
80.
H. Park, P. Bland, A. Hero, and C. Meyer. Least biased target selection in probabilistic atlas construction. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)., volume 2, pages 419–426, 2005.
81.
D. Perperidis, R. Chandrashekara, M. Lorenzo-Valdés, G. Sanchez-Ortiz, A. Rao, D. Rueckert, and R. Mohiaddin. Building a 4d atlas of the cardiac anatomy and motion using mr imaging. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pages 412–415, 2004.
82.
J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever. Mutual information based registration of medical images: a survey. IEEE Transactions on Medical Imaging, 22(8):986–1004, August 2003.CrossRef
83.
C. Pollo, M. Bach Cuadra, O. Cuisenaire, J.-G. Villemure, and J.-P. Thiran. Segmentation of brain structures in presence of a space-occupying lesion. Neuroimage, 24(4):990–996, February 2005.CrossRef
84.
M. Prastawa, E. Bullitt, and N. Moon. Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Radiol., 10(12):1341–1348, 2003.CrossRef
85.
T. Rohlfing, R. Brandt, R. Menzel, D. B. Russakoff, and C. R. Maurer, Jr. Quo vadis, atlas-based segmentation? In J. Suri, D. L. Wilson, and S. Laxminarayan, editors, The Handbook of Medical Image Analysis – Volume III: Registration Models, chapter 11, pages 435–486. Kluwer Academic / Plenum Publishers, 2005.
86.
D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D. Hawkes. Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging, 18(8):712–721, 1999.CrossRef
87.
F. Sanchez Castro, C. Pollo, J. G. Villemure, and T. J. P. Feature-segmentation-based registration for fast and accurate deep brain stimulation targeting. In Proceedings of the 20th International Congress and Exhibition in Computer Assisted Radiology and Surgery, 2006.
88.
F. Sanchez Castro, C. Pollo, J. G. Villemure, and T. J. P. Validation of experts versus atlas-based and automatic registration methods for subthalamic nucleus targeting on mri. International Journal of Computer Assisted Radiology and Surgery, 1(1):5–12, 2006.
89.
J. A. Schnabel, C. Tanner, A. Castellano Smith, M. Leach, R. Hose, D. Hill, and D. Hawkes. Validation of non-rigid registration using finite element methods. In Lecture Notes in Computer Science, Springer Verlag, Berlin, editor, Information Processing in Medical Imaging (IPMI), pages 345–358, 2001.
90.
D. Shattuck, S. Sandor-Leahy, K. Schaper, D. Rottenberg, and R. Leahy. Magnetic resonance image tissue classification using a partial volume model. NeuroImage, 13:856–876, 2001.CrossRef
91.
J. A. Stark and W. J. Fitzgerald. Model-based adaptive histogram equalization. Signal Processing, pages 193–200, 1994.
92.
R. Stefanescu. Parallel nonlinear registration of medical images with a priori information on anatomy and pathology. Thèse de sciences, Université de Nice – Sophia-Antipolis, March 2005.
93.
R. Stefanescu, O. Commowick, G. Malandain, P.-Y. Bondiau, N. Ayache, and X. Pennec. Non-rigid atlas to subject registration with pathologies for conformal brain radiotherapy. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 704–711, 2004.
94.
C. Studholme, D. L. G. Hill, and D. J. Hawkes. Multiresolution voxel similarity measures for mr-pet registrationn. Information Processing in Medical Imaging, pages 287–298, 1995.
95.
G. Subsol, J.-P. Thirion, and N. Ayache. A scheme for automatically building 3D morphometric anatomical atlases: application to a skull atlas. Medical Image Analysis, 2(1):37–60, 1998.CrossRef
96.
J. S. Suri, S. Singh, and L. Reden. Computer vision and pattern recognition techniques for 2-d and 3-d mr cerebral cortical segmentation (part i): A state-of-the-art review. Pattern Analysis and Applications, 5:46–76, 2002.CrossRefMathSciNet
97.
J. Talairach and P. Tournoux. Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system - an approach to cerebral imaging. Thieme Medical Publishers, 1998.
98.
J. Thirion. Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis, 2(3):243–260, 1998.CrossRef
99.
L. Thurjfell, C. Bohm, T. Greitz, and L. Eriksson. Transformations and algorithms in a computerized brain atlas. IEEE Transactions on Nuclear Sciences, 40:1187–1191, 1993.CrossRef
100.
A. W. Toga. Brain Warping. Academic Press, 1999.
101.
G. Unal and G. Slabaugh. Coupled pdes for non-rigid registration and segmentation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 168–175, 2005.
102.
K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens. Automated model-based bias field correction of mr images of the brain. IEEE Transactions on Medical Imaging, 18: 885–896, 1999.CrossRef
103.
K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens. Automated model-based tissue classification of mr images of the brain. IEEE Transactions on Medical Imaging, 18:897–908, 1999.CrossRef
104.
B. C. Vemuri and Y. Chen. Geometric Level Set Methods in Imaging, Vision and Graphics, chapter Joint image registration and segmentation, pages 251–269. Springer Verlag, New York, 2003.
105.
B. C. Vemuri, J. Ye, Y. Chen, and C. M. Leonard. Image registration via level-set motion: Applications to atlas-based segmentation. IEEE Transaction on Medical Image Analysis, 7(1):1–20, 2003.CrossRef
106.
P. Viola and W. Wells. Alignment by maximization of mutual information. Fifth Int. Conf. on Computer Vision, pages 16–23, 1995.
107.
S. K. Warfield, M. Kaus, F. A. Jolesz, and R. Kikinis. Adaptive, template moderated, spatially varying statistical classification. Medical Image Analysis, 4(1):43–55, March 2000.CrossRef
108.
S. K. Warfield, J. Rexilius, P. Huppi, T. Inder, E. Miller, W. Wells, G. Zientara, F. Jolesz, and R. Kikinis. A binary entropy measure to assess nonrigid registration algorithms. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 266–274, 2001.
109.
W. Wells, R. Kikinis, W. Grimson, and F. Jolesz. Adaptive segmentation of mri data. IEEE Transactions on Medical Imaging, 15:429–442, 1996.CrossRef
110.
J. West, J. Fitzpatrick, M. Wang, B. Dawant, C. Maurer Jr, R. Kessler, R. Maciunas, C. Barillot, D. Lemoine, A. Collignon, F. Maes, P. Suetens, D. Vandermeulen, P. van den Elsen, S. Napel, T. Sumanaweera, B. Harkness, P. Hemler, D. Hill, D. Hawkes, C. Studholme, J. Maintz, M. Viergever, G. Malandain, and R. Woods. Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography, 21(4):554–566, 1997.CrossRef
111.
R. Woods, M. Dapretto, N. Sicotte, A. Toga, and J. Mazziotta. Creation and use of a talairach-compatible atlas for accurate, automated, nonlinear intersubject registration, and analysis of functional imaging data. Human Brain Mapping, 8(2-3):73–79, 1999.CrossRef
112.
P. Wyatt and J. A. Noble. Map mrf joint segmentation and registration of medical images. Medical Image Analysis, 7(4):539–552, 2003.CrossRef
113.
C. Xiaohua, M. Brady, and D. Rueckert. Simultaneous segmentation and registration for medical image. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 663–670, 2004.
114.
A. Yezzi, L. Zollei, and T. Kapur. A variational framework for joint segmentation and registration. In Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (CVPR-MMBIA), pages 44–49, 2001.
115.
Y.-N. Young and D. Levy. Registration-based morphing of active contours for segmentation of ct scans. Mathematical Biosciences and Engineering, 2(1):79–96, 2005.CrossRefMATHMathSciNet
116.
E. Zacharaki, D. Shen, A. Mohamed, and C. Davatzikos. Registration of brain images with tumors: Towards the construction of statistical atlases for therapy planning. In IEEE International Symposium on Biomedical Imaging (ISBI), 2006.
117.
Y. Zhan, D. Shen, J. Zeng, L. Sun, G. Fichtinger, J. Moul, and C. Davatzikos. Targeted prostate biopsy using statistical image analysis. IEEE Trans Med Imaging, 26(6):779–88, 2007.CrossRef
118.
L. Zollei, E. Learned Miller, W. Grimson, and W. Wells, III. Efficient population registration of 3d data. In Computer Vision for Biomedical Image Applications., pages 291–301, 2005.
Metadaten
Titel
Atlas-based Segmentation
verfasst von
M. Bach Cuadra
V. Duay
J.-Ph. Thiran
Copyright-Jahr
2015
Verlag
Springer US
Buch
Handbook of Biomedical Imaging

Print ISBN: 978-0-387-09748-0

Electronic ISBN: 978-0-387-09749-7

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-0-387-09749-7_12

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