Cited by 1,587Medical Image Segmentation TechniquesAdvanced Radiotherapy TechniquesMedical Imaging and Analysis

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Deformable Medical Image Registration: A Survey

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Sotiras, A., Davatzikos, C., Paragios, N.·2013·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2013.2265603PMC PMC3745275

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Abstract

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

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Method
Simultaneous PET-MRI: a new approach for functional and morphological imaging
Judenhofer, M. S., Wehrl, H. F., Newport, D. F., Catana, C., Siegel, S. B., Becker, M., Thielscher, A., Kneilling, M., Lichy, M. P., Eichner, M., et al. (2008) · Nature Medicine · doi:10.1038/nm1700 · Cited by 983 · cited 1×
“This is natural since the physical principles behind di erent imaging modalities di er in the anatomical, tissue, organ and functional properties that they may capture, and the diagnosis of a number of diseases is based on leveraging complementary cross-modal information [6].”
Background
Numerical Methods for Image Registration
Jan Modersitzki, C. (2003) · doi:10.1093/acprof:oso/9780198528418.001.0001 · Cited by 926 · cited 1×
“Following [5], currently employed physical models can be further separated in five categories (see Fig.”
Method
Robust registration of 2D and 3D point sets
Fitzgibbon, A. W. (2003) · Image and Vision Computing · doi:10.1016/j.imavis.2003.09.004 · Cited by 925
Shape Matching and Object Recognition Using Low Distortion Correspondences
Berg, A. C., Berg, T. L., & Malik, J. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.320 · Cited by 844 · cited 1×
“[288] formulated the problem of recovering feature”
Molecular imaging in drug discovery and development
Rudin, M., & Weissleder, R. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1007 · Cited by 811 · cited 1×
“The exact same concept is applicable when developing and evaluating new drugs where imaging studies need to be normalized and mapped to the same space towards interpretation [17].”
Background
Multi-modality image registration by maximization of mutual information
Maes, F., Collignon, A. M. F., Vandermeulen, D., Marchal, G., & Suetens, P. (1996) · IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) · doi:10.1109/mmbia.1996.534053 · Cited by 809
A SURVEY OF NONLINEAR CONJUGATE GRADIENT METHODS
Hager, W. W., & Zhang, H. (2005) · Cited by 779
Projective image restoration using sparsity regularization
Anantrasirichai, N., Burn, J. F., & Bull, D. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738223 · Cited by 735
MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration
Heinrich‬, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, M., & Schnabel, J. A. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.05.008 · Cited by 703 · cited 1×
“[373] presented a new descriptor for multi-modal registration.”
Background
Optimization of mutual information for multiresolution image registration
Unser, M. A., & Thévenaz, P. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.887976 · Cited by 694 · cited 1×
“Based on the LM algorithm, Thevanez and Unser [413] proposed an efﬁcient optimizer for mutual information driven registration.”
Method
Robust Point Set Registration Using Gaussian Mixture Models
Jian, B., & Vemuri, B. C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.223 · Cited by 682 · cited 1×
“[300] modeled each point set using GMMs and used a distance to compare them.”
Image registration by maximization of combined mutual information and gradient information
Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.876307 · Cited by 670
A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets
Castillo, R., Castillo, E., Guerra, R., Johnson, V., McPhail, T., Garg, A. K., & Guerrero, T. (2009) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/7/001 · Cited by 645 · cited 1×
“One may cite the POPI model [452] containing 40 landmarks in every frame of a 4 D lung CT acquisition, or the 4 D CT dataset made available by Castillo et al. [453] with landmarks in the maximum inhale and exhale phase.”
Method
Consistent image registration
Christensen, G. E., & Johnson, H. J. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.932742 · Cited by 638 · cited 8×
“to tackle this shortcoming, Christensen and Johnson [19] pro-”
BackgroundInfluential
ICP registration using invariant features
Sharp, G. C., Lee, S. W., & Wehe, D. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982886 · Cited by 620 · cited 1×
“[316] investigated the use of shape features in addition to the positional information when estimating the correspondences.”
Background
Handbook of Mathematical Models in Computer Vision
(2006) · doi:10.1007/0-387-28831-7 · Cited by 574
The Scaling and Squaring Method for the Matrix Exponential Revisited
Higham, N. J. (2005) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/04061101x · Cited by 549
The correlation ratio as a new similarity measure for multimodal image registration
Roche, A., Malandain, G., Pennec, X., & Ayache, N. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056301 · Cited by 549 · cited 1×
“[262] argued that the generality of mutual information can be a drawback when a reasonable hypothesis can be made regarding the relationship between the intensities.”
Background
Line search algorithms with guaranteed sufficient decrease
Morè, J. J., & Thuente, D. J. (1994) · ACM Transactions on Mathematical Software · doi:10.1145/192115.192132 · Cited by 535 · cited 2×
“The rst employed a function of the step size that decayed with each iteration, while the second was based on the inexact line search algorithm of Moré and Thuente [372].”
Method
Three-dimensional modeling for functional analysis of cardiac images, a review
Frangi, A. F., Niessen, W. J., & Viergever, M. A. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906421 · Cited by 533 · cited 1×
“cardiac and respiratory motion) the key element in diagnosis becomes their spatio-temporal behavior [1, 2].”
Background
Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint
Rohlfing, T., Maurer, C., Bluemke, D., & Jacobs, M. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.814791 · Cited by 529 · cited 1×
“In [238], such a strategy was employed to register contrast-enhanced MR breast images.”
Method
IEEE International Conference on Computer Vision and Pattern Recognition
Daubney, B., Gibson, D., & Campbell, N. (2008) · Cited by 521
Landmark matching via large deformation diffeomorphisms
Joshi, S. C., & Miller, M. I. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.855431 · Cited by 500 · cited 2×
“The advantage of this framework, known as Large Deformation Di eomorphic Metric Mapping (LDDMM), is that it allows for the de nition of a distance between images or sets of points [70, 71].”
Background
Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation
Ashburner, J., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.049 · Cited by 494 · cited 3×
“The Gauss-Newton optimizer has been used in [81, 236, 245].”
Method
Automated multi-modality image registration based on information theory
Collignon, A. M. F., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., & Marchal, G. (1995) · Cited by 485
Fast parametric elastic image registration
Kybic, J., & Unser, M. A. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.813139 · Cited by 473 · cited 2×
“Unser [136] compared the LM algorithm with GD, GD with a”
BackgroundMethod
Dynamic contrast‐enhanced MRI in clinical oncology: Current status and future directions
Padhani, A. R. (2002) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10176 · Cited by 470 · cited 1×
“perfusion imaging for cancer characterization and treatment) that convey great clinical information and have important diagnostic value [3, 4].”
Background
Overview of image-guided radiation therapy
Xing, L., Thorndyke, B., Schreibmann, E., Yang, Y., Li, T., Kim, G.-Y., Luxton, G., & Koong, A. C. (2006) · Medical dosimetry · doi:10.1016/j.meddos.2005.12.004 · Cited by 469
Deformable registration of diffusion tensor MR images with explicit orientation optimization
ZHANG, H., Yushkevich, P. A., Alexander, D. C., & Gee, J. C. (2006) · Medical Image Analysis · doi:10.1016/j.media.2006.06.004 · Cited by 461 · cited 1×
“[158] tackled di usion tensor registration by taking into consideration tensor reorientation.”
Background
A Log-Euclidean Framework for Statistics on Diffeomorphisms
Arsigny, V., Commowick, O., Pennec, X., & Ayache, N. (2006) · Lecture notes in computer science · doi:10.1007/11866565_113 · Cited by 461 · cited 1×
“Stationary velocity fields [96] have been used towards this direction.”
Method
Data fusion through cross-modality metric learning using similarity-sensitive hashing
Bronstein, M. M., Bronstein, A. M., Michel, F., & Nikos Paragios, A. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539928 · Cited by 461 · cited 1×
“presented a supervised technique whose aim was to learn a similarity metric that discerns between corresponding and noncorresponding points [376].”
The distribution of target registration error in rigid-body point-based registration
Fitzpatrick, J. M., & West, J. B. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.952729 · Cited by 459 · cited 1×
“Some resent studies regarding the errors in the process are given in [284]–[286].”
Background
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
Granger, S., & Pennec, X. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47979-1_28 · Cited by 454 · cited 5×
“Pennec et al. [23] dropped the linearity assumption by modeling the deformation process through the St Venant-Kirchoff elasticity energy that extends the linear elastic model to the nonlinear regime.”
MethodInfluential
Variational problems on flows of diffeomorphisms for image matching
Dupuis, P., Grenander, U., & Miller, M. I. (1998) · Quarterly of Applied Mathematics · doi:10.1090/qam/1632326 · Cited by 447 · cited 1×
“In this case, the deformation is modeled by considering its velocity over time according to the Lagrange transport equation [35,65,66].”
Method
A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations
Schnabel, J., Rueckert, D., Quist, M., Blackall, J., Castellano-Smith, A. D., Hartkens, T., Penney, G., Hall, W., Liu, H.-Y., Truwit, C., et al. (2001) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/3-540-45468-3_69 · Cited by 442 · cited 2×
“[140] proposed to usemultilevel B-splines.”
Method
Volumetric transformation of brain anatomy
Christensen, G. E., Joshi, S. C., & Miller, M. I. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.650882 · Cited by 441 · cited 1×
“[31] presented a hierarchical way to recover the transformations for brain anatomy.”
Background
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
Murphy, K., van Ginneken, B., Reinhardt, J. M., Kabus, S., Ding, K., Deng, X. H., Cao, K., Du, K., Christensen, G. E., Garcia, V., et al. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2158349 · Cited by 441 · cited 1×
“The development of dedicated methods for reference standard construction [454] and the organization of registration 2 http://www.mindboggle.info/papers/evaluation NeuroImage2009/data/ challenges [455] create the necessary conditions for objective comparison of registration methods.”
Method
Functional tumor imaging with dynamic contrast‐enhanced magnetic resonance imaging
Choyke, P. L., Dwyer, A. J., & Knopp, M. V. (2003) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.10304 · Cited by 440 · cited 1×
“perfusion imaging for cancer characterization and treatment) that convey great clinical information and have important diagnostic value [3, 4].”
Background
Direct manipulation of free-form deformations
Hsu, W. M., Hughes, J. F., & Kaufman, H. (1992) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/133994.134036 · Cited by 438 · cited 1×
“graphics community [132], [133] but gained wide acceptance in the medical image analysis community when coupled with cubic- splines [134]–[137].”
Background
On the Metrics and Euler-Lagrange Equations of Computational Anatomy
Miller, M. I., Trouvé, A., & Laurent Younes, B. (2002) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.4.092101.125733 · Cited by 437 · cited 1×
“The distance between these elements is defined as a geodesic, according to a metric, that connects them and can be used for studies of anatomical variability [70].”
Background
Compactly supported positive definite radial functions
Wu, Z. (1995) · Advances in Computational Mathematics · doi:10.1007/bf03177517 · Cited by 431 · cited 1×
“Other local support radial basis functions include the C(2) smooth Wu functions [124] and the functions proposed by Buhmann [125].”
Background
Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines
Klein, S., Staring, M., & Pluim, J. P. W. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.909412 · Cited by 429 · cited 6×
“[400] studied two variants of gradient descent.”
MethodInfluential
Dense image registration through MRFs and efficient linear programming
Glocker, B., Nikos Komodakis, A., Tziritas, G., Navab, N., & Nikos Paragios, A. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.03.006 · Cited by 428 · cited 1×
“FastPD was rst used for image registration to infer the displacements of a grid-based deformation model in [412, 413].”
Method
Advances in Image-Guided Radiation Therapy
Dawson, L. A., & Jaffray, D. A. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2006.09.9515 · Cited by 425 · cited 1×
“In the case of radiation therapy, registration can be used to account for the patient's position and motion, allowing for the necessary adjustments to dose and aiming angles that maximize the therapeutic gain [13 15].”
Background
A Correlation-Based Approach to Robust Point Set Registration
Tsin, Y., & Takeo Kanade, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24672-5_44 · Cited by 424 · cited 1×
“Tsin and Kanade [298] proposed to register point sets based on a measure called kernel correlation.”
Background
Landmark-based elastic registration using approximating thin-plate splines
Rohr, K., Stiehl, H. S., Sprengel, R., Buzug, T. M., Weese, J., & Kuhn, M. H. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.929618 · Cited by 424 · cited 3×
“Furthermore, TPS do not take into consideration possible errors in the estimation of the displacements in the landmark positions [110].”
Background
Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration
Rueckert, D., Frangi, A. F., & Schnabel, J. A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815865 · Cited by 414 · cited 2×
“demonstrated that the model in [174] can encode landmark position information [176].”
Background
Optical Flow Estimation
Fleet, D. J., & Weiss, Y. (2005) · Springer eBooks · doi:10.1007/0-387-28831-7_15 · Cited by 406 · cited 1×
“cations where the interest is to recover the apparent motion of objects between sequences of successive images (optical flow estimation) [12], [13].”
Background
Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable
Torsten Rohlfing, B. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2163944 · Cited by 405 · cited 1×
“…for image registration ( i.e. , Non-rigid Image Regis-tration Evaluation Project - NIREP [450]) and the increasing understanding regarding the use of surrogate measures for the measurement of the accuracy of registration [451] will further facilitate the comparison between different algorithms.”
Background
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
Goshtasby, A. A. (2005) · Cited by 404
Medical Image Registration
Hajnal, J. V., & Hill, D. L. G. (2001) · Lecture notes in computer science · doi:10.1007/978-3-032-25169-5 · Cited by 402
The adaptive bases algorithm for intensity-based nonrigid image registration
Rohde, G. K., Aldroubi, A., & Dawant, B. M. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819299 · Cited by 401 · cited 1×
“[125] applied the Wu functions in image registration and derived bounds for”
Method
Consistent landmark and intensity-based image registration
Johnson, H. J., & Christensen, G. E. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.1009381 · Cited by 400 · cited 2×
“Johnson and Christensen’s consistent registration approach [379] as well as Rueckert et al.”
BackgroundMethod
DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting
Ou, Y., Sotiras, A., Nikos Paragios, A., & Davatzikos, C. A. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.07.002 · Cited by 399
Self-citation
Fast Fluid Registration of medical images
Bro‐Nielsen, M., & Gramkow, C. (1996) · Lecture notes in computer science · doi:10.1007/bfb0046964 · Cited by 396 · cited 3×
“Instead of devising a numerical scheme to solve the PDE that results from the equilibrium equation (5), recursive convolutions with an appropriate Green's function were used following [37].”
MethodInfluential
Feature Correspondence Via Graph Matching: Models and Global Optimization
Torresani, L., Kolmogorov, V., & Rother, C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_44 · Cited by 396 · cited 1×
“also employed pairwise constraints to model local spatial coherence [289].”
Method
Handbook of Medical Imaging. Processing and Analysis
Bankman, I. N., & Morcovescu, Ş. (2002) · Medical Physics · doi:10.1118/1.1429630 · Cited by 396
Deformable Registration of Diffusion Tensor MR Images with Explicit Orientation Optimization
Zhang, H. G., Yushkevich, P. A., & Gee, J. C. (2005) · Lecture notes in computer science · doi:10.1007/11566465_22 · Cited by 389
Surface Matching via Currents
Vaillant, M., & Glaunès, J. (2005) · Lecture notes in computer science · doi:10.1007/11505730_32 · Cited by 389 · cited 1×
“Among its extensions, one may cite volume registration for scalar [67], [77]–[79] vector- [80] and tensor-valued data [81], point-matching [68], point-matching on spheres [82], matching sets of unlabeled points [83]–[85], shape-matching [65], [86], curve-mapping [87]–[90], and hybrid registration [91], [92].”
Background
Variational Methods for Multimodal Image Matching
Hermosillo, G., Chefd'Hotel, C., & Faugeras, O. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020830525823 · Cited by 388
Adaptive Stochastic Gradient Descent Optimisation for Image Registration
Klein, S., Pluim, J. P. W., Staring, M., & Viergever, M. A. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0168-y · Cited by 387 · cited 1×
“Klein et al. [414] presented an adaptive step mechanism, while Bhagalia et al. [415] proposed an edge-driven importance sampling to improve the gradient approximation.”
Background
Non-parametric Diffeomorphic Image Registration with the Demons Algorithm
Vercauteren, T. K., Pennec, X., Aymeric Perchant, B., & Ayache, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75759-7_39 · Cited by 380 · cited 2×
“In [44], Vercauteren et al. proposed a variant of Thirion’s algorithm endowed with the diffeomorphic property.”
BackgroundMethod
Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models
Davatzikos, C. A. (1997) · Computer Vision and Image Understanding · doi:10.1006/cviu.1997.0605 · Cited by 379 · cited 1×
“Davatzikos [18] first used geometric characteristics to establish a mapping between the cortical surfaces.”
MethodSelf-citation
Probabilistic graph and hypergraph matching
Zass, R., & Shashua, A. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587500 · Cited by 373 · cited 1×
“Zass and Shashua in [291] proposed a similar formulation, while using a different optimization method.”
Method
Geodesic Shooting for Computational Anatomy
Miller, M. I., Trouvé, A., & Laurent Younes, B. (2006) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-005-3624-0 · Cited by 366
A Review of Geometric Transformations for Nonrigid Body Registration
Holden, M. P. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.904691 · Cited by 365 · cited 1×
“The rest of the section is organized by following coarsely and extending the classiﬁcation of deformation models given by Holden [14].”
Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation
Olivier Clatz, A., Maxime Sermesant, B., Bondiau, P., Delingette, H., Warfield, S. K., Malandain, G., & Ayache, N. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.857217 · Cited by 363 · cited 1×
“In [181], after an initial a ne registration between a normal atlas and the patient's image, a coupled model that predicts the anisotropic evolution of the tumor as well as its mass-e ect was applied.”
Method
A review of cardiac image registration methods
Mäkelä, T. J., Clarysse, P., Sipilä, O., Pauna, N., Pham, Q. C., Katila, T. E., & Magnin, I. E. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.804441 · Cited by 363 · cited 1×
“cardiac and respiratory motion) the key element in diagnosis becomes their spatio-temporal behavior [1, 2].”
Background
A Tensor-Based Algorithm for High-Order Graph Matching
Duchenne, O., Bach, F., Kweon, I.-S., & Ponce, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.110 · Cited by 357
Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach
Vercauteren, T. K., Pennec, X., Aymeric Perchant, B., & Ayache, N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_90 · Cited by 356 · cited 4×
“In the demons registration setting, an extension of Gauss-Newton by Malis [43] was employed to derive the symmetric demons forces [47], [50].”
BackgroundMethodInfluential
Error Estimates for Interpolation by Compactly Supported Radial Basis Functions of Minimal Degree
Wendland, H. (1998) · Journal of Approximation Theory · doi:10.1006/jath.1997.3137 · Cited by 353 · cited 1×
“[121] investigated the use of Wendland functions [122, 123] that exhibit the desired locality property, for elastic registration.”
Background
The dual-bootstrap iterative closest point algorithm with application to retinal image registration
Stewart, C. V., Tsai, C.-L., & Roysam, B. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819276 · Cited by 347 · cited 2×
“[241] proposed a dual-bootstrap ICP method to register retinal images.”
Method
Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration
Yeo, B. T. T., Sabuncu, M. R., Vercauteren, T. K., Ayache, N., Fischl, B. R., & Golland, P. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2030797 · Cited by 345 · cited 2×
“The application of the Demons algorithm is not limited to scalar images and has been extended to di usion tensor images [57], multi-channel ones [58] as well as di erent geometries [59].”
Method
Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention
Wein, W., Brunke, S. S., Khamene, A., Callstrom, M. R., & Navab, N. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.06.006 · Cited by 341 · cited 1×
“Wein et al. [364] simulated an US image in order to tackle the problem of CT-to-US rigid/afﬁne registration.”
Background
Group Actions, Homeomorphisms, and Matching: A General Framework
Miller, M. I., & Laurent Younes, B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011161132514 · Cited by 332 · cited 2×
“A number of theoretical aspects of this framework and especially the ones related with computational analysis were further developed in [71]–[75].”
Background
Intensity-Based Image Registration by Minimizing Residual Complexity
Myronenko, A., & Song, X. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2053043 · Cited by 329 · cited 1×
“Myronenko and Song proposed to use residual complexity (RC) to account for complex spatially-varying intensity distortions [327].”
Background
Optimal registration of deformed images
Broit, C. (1981) · Scholarly Commons (University of Pennsylvania) · Cited by 329
Diffeomorphisms Groups and Pattern Matching in Image Analysis
Trouvé, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008001603737 · Cited by 325 · cited 1×
“In this case, the deformation is modeled by considering its velocity over time according to the Lagrange transport equation [35,65,66].”
Method
An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization
Grimson, W. E. L., Ettinger, G. J., White, S. J., Lozano‐Pérez, T., Wells, W. M., & Kikinis, R. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.491415 · Cited by 321
Diffeomorphic Registration Using B-Splines
Rueckert, D., Aljabar, P., Heckemann, R. A., Hajnal, J. V., & Hammers, A. (2006) · Lecture notes in computer science · doi:10.1007/11866763_86 · Cited by 319 · cited 2×
“[138] imposed the hard constraints proven in [139] to produce diffeomorphic deformation fields.”
Background
Global Convergence of a Cass of Quasi-Newton Methods on Convex Problems
Byrd, R. H., Nocedal, J. M., & Yuan, Y.-X. (1987) · SIAM Journal on Numerical Analysis · doi:10.1137/0724077 · Cited by 319
Dual-modality PET/CT imaging: the effect of respiratory motion on combined image quality in clinical oncology
Beyer, T., Antoch, G., Blodgett, T. M., Freudenberg, L., Akhurst, T. J., & Mueller, S. (2003) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-002-1097-6 · Cited by 298 · cited 1×
“PET-CT fusion is the most natural example of such a need as demonstrated by the development of integrated scanners that can do parallel co-registered acquisitions of both modalities [7].”
Background
Image Similarity Using Mutual Information of Regions
Russakoff, D. B., Tomasi, C., Torsten Rohlfing, B., & Maurer, C. R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24672-5_47 · Cited by 295 · cited 1×
“[280] introduced Regional Mutual Information that pushed forward the previous idea by taking into account co-occurrences between regions.”
Background
A Revolution: Belief Propagation in Graphs with Cycles
Frey, B. J., & Mackay, D. (1997) · Cited by 291
Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation
Ledesma‐Carbayo, M. J., Kybic, J., Desco, M., Santos, A., Sühling, M., Hunziker, P., & Unser, M. A. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.852050 · Cited by 290
Curvature Based Image Registration
Fischer, B., & Jan Modersitzki, C. (2003) · Journal of Mathematical Imaging and Vision · doi:10.1023/a:1021897212261 · Cited by 289 · cited 1×
“Fischer and Modersitzki used this constraint in [58], [59].”
Method
Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information
Roche, A., Pennec, X., Malandain, G., & Ayache, N. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.959301 · Cited by 289 · cited 1×
“[363] tackled ultrasound (US) to MR rigid registration by simulating an US image from the MR one.”
Background
The softassign Procrustes matching algorithm
Rangarajan, A., Chui, H., & Bookstein, F. L. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63046-5_3 · Cited by 285
FAIR
Modersitzki, J. (2009) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718843 · Cited by 279
The theory and practice of Bayesian image labeling
Chou, P. B., & Brown, C. M. (1990) · International Journal of Computer Vision · doi:10.1007/bf00054995 · Cited by 278 · cited 1×
“In the rst case, one may cite Iterated Conditional Modes (ICM) [383] and Highest Con dence First (HCF) [384, 385].”
Background
A viscous fluid model for multimodal non-rigid image registration using mutual information
D’Agostino, E., Maes, F., Vandermeulen, D., & Suetens, P. (2003) · Medical Image Analysis · doi:10.1016/s1361-8415(03)00039-2 · Cited by 278
MRF Energy Minimization and Beyond via Dual Decomposition
Nikos Komodakis, A., Nikos Paragios, A., & Tziritas, G. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.108 · Cited by 278 · cited 1×
“The inference was performed by a dual decomposition based method [333].”
Method
A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina
Can, A., Stewart, C. V., Roysam, B., & Tanenbaum, H. L. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990136 · Cited by 275 · cited 1×
“used the branching and crossover points of the blood vessel structure as feature points [276].”
Method
Improving vision-based control using efficient second-order minimization techniques
Malis, E. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1308092 · Cited by 268 · cited 2×
“In the demons registration setting, an extension of Gauss-Newton by Malis [43] was employed to derive the symmetric demons forces [47], [50].”
BackgroundMethod
A physics-based coordinate transformation for 3-D image matching
Davis, M. H., Khotanzad, A. R., Flamig, D. P., & Harms, S. E. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.585766 · Cited by 267 · cited 1×
“[130].”
Applications of entropic spanning graphs
Hero, A. O., Ma, B., Michel, O., & Gorman, J. (2002) · IEEE Signal Processing Magazine · doi:10.1109/msp.2002.1028355 · Cited by 265 · cited 1×
“In [270, 271], a Minimum Spanning Tree (MST) was used to estimate the RE and the optimal transformation was estimated by minimizing it.”
Method
Neuronavigation by Intraoperative Three-dimensional Ultrasound: Initial Experience during Brain Tumor Resection
Unsgaard, G., Ommedal, S., Müller, T. B., Grønningsaeter, A., & Hernes, T. A. N. (2002) · Neurosurgery · doi:10.1097/00006123-200204000-00022 · Cited by 262
Image-guidance for surgical procedures
Peters, T. M. (2006) · Physics in Medicine and Biology · doi:10.1088/0031-9155/51/14/r01 · Cited by 257 · cited 1×
“In the case of surgical operations, providing a continuous real-time mapping between interventional data (often 2D) and images used for planning (CT/MR) would have a great impact and help ensure the success of surgery [10 12].”
Background
A mixture model for representing shape variation
Cootes, T. F., & Taylor, C. (1999) · Image and Vision Computing · doi:10.1016/s0262-8856(98)00175-9 · Cited by 256 · cited 1×
“Given the correspondences, a solution that consists of translating, rotating and scaling can be analytically calculated [295].”
Background
Nonrigid Image Registration Using Conditional Mutual Information
Loeckx, D., Slagmolen, P., Maes, F., Vandermeulen, D., & Suetens, P. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2021843 · Cited by 256 · cited 2×
“[349] proposed to condition the evaluation of MI upon the position.”
Background
Validation of nonrigid image registration using finite-element methods: application to breast MR images
Schnabel, J. A., Tanner, C., Castellano-Smith, A. D., Degenhard, A., Leach, M. O., Hose, D. R., Hill, D., & Hawkes, D. J. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.808367 · Cited by 255
An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects
Hogea, C. S., Davatzikos, C. A., & Biros, G. (2007) · Journal of Mathematical Biology · doi:10.1007/s00285-007-0139-x · Cited by 253 · cited 1×
“[196] addressed the case of Glioblastoma multiforme brain tumors modeled previously [184].”
BackgroundSelf-citation
Iconic feature based nonrigid registration: the PASHA algorithm
Cachier, P., Bardinet, É., Dormont, D., Pennec, X., & Ayache, N. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00002-x · Cited by 251 · cited 1×
“[42] proposed, to relate symmetric Demons forces with the efficient second-order minimization (ESM) [43].”
Background
Global Stereo Reconstruction under Second-Order Smoothness Priors
Woodford, O. J., Torr, P. H. S., Reid, I. D., & Fitzgibbon, A. W. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.131 · Cited by 247
Understanding the “Demon’s Algorithm”: 3D Non-rigid Registration by Gradient Descent
Pennec, X., Cachier, P., & Ayache, N. (1999) · Lecture notes in computer science · doi:10.1007/10704282_64 · Cited by 241 · cited 1×
“[40] studied image registration as an energy”
Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach
Metz, C., Klein, S., Schaap, M., van Walsum, T., & Niessen, W. J. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.10.003 · Cited by 237 · cited 1×
“FFDs have been extended to tackle multiple-image registration where hard constraints are employed to deﬁne a reference domain [146]–[149].”
Background
Evaluation of three‐dimensional finite element‐based deformable registration of pre‐ and intraoperative prostate imaging
Bharatha, A., Hirose, M., Hata, N., Warfield, S. K., Ferrant, M., Zou, K. H., Suárez-Santana, E., Ruiz‐Alzola, J., D'Amico, A. V., Cormack, R. A., et al. (2001) · Medical Physics · doi:10.1118/1.1414009 · Cited by 233 · cited 1×
“Rohde et al. [125] applied the Wu functions in image registration and derived bounds for the basis function’s coefﬁcients so that the Jacobian of the computed transformation remains positive.”
Background
Approximate Thin Plate Spline Mappings
Donato, G., & Belongie, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47977-5_2 · Cited by 232 · cited 4×
“Threeways to address the computational problems related with the presence of a great number of landmarks were studied by Donato and Belongie [111].”
BackgroundInfluential
Image Warping by Radial Basis Functions: Application to Facial Expressions
Arad, N., Dyn, N., Reisfeld, D., & Yeshurun, Y. (1994) · CVGIP Graphical Models and Image Processing · doi:10.1006/cgip.1994.1015 · Cited by 232 · cited 1×
“Gaussian functions (φ(r) = e− r2 σ2 ) is another class of RBFs that can be used to parametrize the deformation [119].”
Background
Vessel enhancing diffusionA scale space representation of vessel structures
Manniesing, R., Viergever, M. A., & Niessen, W. J. (2006) · Medical Image Analysis · doi:10.1016/j.media.2006.06.003 · Cited by 231 · cited 1×
“That is why a number of task-tailored detectors have been devised [280]–[283].”
Background
Ill-posed medicine—an introduction to image registration
Fischer, B., & Modersitzki, J. (2008) · Inverse Problems · doi:10.1088/0266-5611/24/3/034008 · Cited by 231
FAIR: Flexible Algorithms for Image Registration
Modersitzki, J. (2009) · Cited by 227
Book Review: 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications
Ray, L. A. (2005) · Journal of Electronic Imaging · doi:10.1117/1.2041647 · Cited by 224
Direct manipulation of free-form deformations
Hsu, W. M., Hughes, J. F., & Kaufman, H. (1992) · ACM SIGGRAPH Computer Graphics · doi:10.1145/142920.134036 · Cited by 224
Approximate Labeling via Graph Cuts Based on Linear Programming
Nikos Komodakis, A., & Tziritas, G. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1061 · Cited by 223 · cited 1×
“We are going to brie y present two methods that have been used in registration tasks, Fast-PD [409,410] and TRW-S [411].”
Method
Large Deformation Diffeomorphic Metric Curve Mapping
Glaunès, J., Qiu, A., Miller, M. I., & Laurent Younes, B. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0141-9 · Cited by 223

Flagged first, then by citations

13 of the 539 references couldn’t be matched to a known paper.

Research works printed but not linked: 7

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Sorted by citations

Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
“Noticeably, it is very difficult to learn meaningful features given such inaccurate correspondences derived from imperfect image registration, as suffered by many supervised learning methods (79, 80, 81).”
Background
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J. V., & Dalca, A. V. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2897538 · Cited by 2,334
“Most existing deformable registration algorithms iteratively optimize a transformation based on an energy function [7].”
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MethodInfluential
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Image Matching from Handcrafted to Deep Features: A Survey
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Background

See all 1,587

Authors: Sotiras, A., Davatzikos, C., & Paragios, N.
Affiliations: University of Pennsylvania (US)
ORCID: Sotiras, A.
Davatzikos, C.
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2013-07
Volume: 32
Issue: 7
Pages: 1153-1190
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2013.2265603
PMC: PMC3745275
OpenAlex: W2115167851
Cited by: 1,587
References: 539
Citations by year: 2026: 81 · 2025: 121 · 2024: 115 · 2023: 96 · 2022: 115 · 2021: 142 · 2020: 136 · 2019: 126 · 2018: 152 · 2017: 153 · 2016: 130 · 2015: 123 · 2014: 86 · 2013: 9
FWCI: 18.05
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
HAL (submittedVersion, other-oa)
Funders: National Institute on Aging (R01AG014971)
National Institute of Mental Health (R01MH070365)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords: deformable image registration, medical image registration, multimodal image fusion, statistical atlas, population models, anatomical variation
MeSH terms: Algorithms, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted

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Deformable Medical Image Registration: A Survey

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