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Deformable Medical Image Registration: A Survey
Open accessAbstract
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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“deformable image registration (Sotiras et al., 2013) is a core component of all MAS methods, and the quality of individual registrations has a very high impact on the accuracy of the final segmentation.”
From MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2016) · cites this paper as Sotiras et al., 2013
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Items 401–526 of 526
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BackgroundA Stochastic Iterative Closest Point Algorithm (stochastICP)
Penney, G., Edwards, P., King, A. P., Blackall, J. M., Batchelor, P. G., & Hawkes, D. J. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45468-3_91 · Cited by 44 · cited 1×
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BackgroundTemporal subtraction of thorax CR images using statistical deformation model
Loeckx, D., Maes, F., Vandermeulen, D., & Suetens, P. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819291 · Cited by 44 · cited 1×
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MethodAccelerated Nonrigid Intensity-Based Image Registration Using Importance Sampling
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BackgroundLarge Deformation Inverse Consistent Elastic Image Registration
He, J., & Christensen, G. E. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45087-0_37 · Cited by 43 · cited 11×
“In a subsequent work, Christensen et al. [31] presented a hierarchical way to recover the transformations for brain anatomy.”
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BackgroundAn information theoretic approach for non-rigid image registration using voxel class probabilities
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Multi-modal diffeomorphic demons registration based on point-wise mutual information
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Peyrat, J., Delingette, H., Maxime Sermesant, B., Pennec, X., Xu, C., & Ayache, N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_117 · Cited by 39 · cited 2×
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Method3D Knowledge-Based Segmentation Using Pose-Invariant Higher-Order Graphs
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BackgroundEvaluation of 3D modality-independent elastography for breast imaging: a simulation study
Ou, J. J., Ong, R. E., Yankeelov, T. E., & Miga, M. I. (2007) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/1/010 · Cited by 38 · cited 1×
“Image driven approaches have been proposed toward estimating subject-specific tissue properties [213]–[215].”
BackgroundLandmark and Intensity-Based, Consistent Thin-Plate Spline Image Registration
Johnson, H. J., & Christensen, G. E. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45729-1_33 · Cited by 37 · cited 3×
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BackgroundCommowick, O., Stefanescu, R., Fillard, P., Arsigny, V., Ayache, N., Xavier Pennec, B., & Malandain, G. (2005) · Lecture notes in computer science · doi:10.1007/11566489_114 · Cited by 37 · cited 1×
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BackgroundImage Registration and Segmentation by Maximizing the Jensen-Rényi Divergence
Hamza, A. B., & Krim, H. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45063-4_10 · Cited by 36 · cited 1×
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BackgroundShih, T., Chen, J., Liu, D., Nie, K., Sun, L., Lin, M., Chang, D., Nalcioğlu, O., & Su, M. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/14/013 · Cited by 36 · cited 1×
“Other modeling choices include the inclusion of friction between breast and plates in the contact model [209, 212, 218] or do not (frictionless) [208, 211, 214, 215].”
BackgroundHipwell, J. H., Tanner, C., Crum, W. R., Schnabel, J. A., & Hawkes, D. J. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.903569 · Cited by 36
Approximation and interpolation employing divergence-free radial basis functions with applications
Lowitzsch, S. (2004) · OakTrust (Texas A&M University Libraries) · Cited by 36
Spatial Information in Entropy-Based Image Registration
Sabuncu, M. R., & Ramadge, P. J. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39701-4_14 · Cited by 35 · cited 1×
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BackgroundInformation Theoretic Deformable Registration Using Local Image Information
Karaçalı, B. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-8704-0 · Cited by 35 · cited 1×
“This approach was investigated by Hermosillo et al. [191] and Karac¸ali [346].”
MethodA General Fast Registration Framework by Learning Deformation–Appearance Correlation
Kim, M. J., Guorong Wu, A., Yap, P., & Shen, D. G. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2170698 · Cited by 35 · cited 1×
“[173] used support vector regression models to predict the interme-”
MethodWörz, S., & Rohr, K. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.12.003 · Cited by 35 · cited 2×
“W¨orz and Rohr extended Gaussian EBS in [131].”
BackgroundRegistration Using Sparse Free-Form Deformations
Shi, W., Zhuang, X., Pizarro, L., Bai, W., Wang, H., Tung, K.-P., Edwards, P., & Rueckert, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33418-4_81 · Cited by 34 · cited 1×
“[142] used the multi-level B-splines model of [140] while imposing that only a sparse subset of the control points is active.”
MethodBreast Image Registration by Combining Finite Elements and Free-Form Deformations
Lee, A., Schnabel, J. A., Rajagopal, V., Nielsen, P. M. F., & Nash, M. P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13666-5_99 · Cited by 34 · cited 1×
“FEMs have been used either to register the images [200], [204] or to provide a more appropriate initialization for standard intensity-based nonrigid registration methods [205]–[207].”
MethodDense Registration with Deformation Priors
Glocker, B., Komodakis, N., Navab, N., Tziritas, G., & Paragios, N. (2009) · Information Processing in Medical Imaging · doi:10.1007/978-3-642-02498-6_45 · Cited by 34 · cited 2×
“extended this method to tackle atlas-based registration in [436], and to knowledge-based registration with the use of learned pairwise relations in [177].”
BackgroundMethodDiffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
Marc Modat, A., Vercauteren, T. K., Ridgway, G. R., Hawkes, D. J., Fox, N. C., & Sebastien Ourselin, A. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.843962 · Cited by 34 · cited 1×
“Other efforts include the encoding of similarity metrics such as Normalized Mutual Information by Trist´an-Vega et al. [53] and Modat et al. [54].”
MethodBiomechanically-Constrained 4D Estimation of Myocardial Motion
Sundar, H., Davatzikos, C. A., & Biros, G. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04271-3_32 · Cited by 33
Self-citationTopology preservation evaluation of compact-support radial basis functions for image registration
Yang, X., Xue, Z., Liu, X., & Xiong, D. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.03.004 · Cited by 33 · cited 2×
“[103] compared the previous locally constrained radial basis functions by using transformations on random point sets, artificial images and medical images.”
BackgroundCámara, Ó., Delso, G., Colliot, O., Ingelmo, A. M., & Isabelle Bloch, A. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.889712 · Cited by 33 · cited 1×
“The methods were used in a sequential manner. c) Exploiting segmented structures information: Camara et al. [384] presented an approach where the result of the registration of segmented structures is refined by iconic registration.”
MethodPrimal/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation
Glocker, B., Nikos Komodakis, A., Nikos Paragios, A., Gläser, C., Tziritas, G., & Navab, N. (2007) · Springer eBooks · doi:10.1007/978-3-540-75759-7_65 · Cited by 33 · cited 1×
“This method was extended to tackle atlas-based registration in [414].”
MethodGoshtasby, A. (2012) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-1-4471-2458-0 · Cited by 32
Point Set Registration Using Havrda–Charvat–Tsallis Entropy Measures
Tustison, N. J., Awate, S. P., Song, G., Cook, T. S., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2086065 · Cited by 32 · cited 1×
“also used a GMM with the difference that the Gaussians were not isotropic [305].”
MethodOptical flow estimation with adaptive convolution kernel prior on discrete framework
Lee, K. J., Kwon, D., Yun, I., & Lee, S.-U. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539953 · Cited by 32 · cited 1×
“[420] used TRW-S to solve the optical ow estimation problem based on an adaptive convolution kernel prior.”
MethodLarge breast compressions: Observations and evaluation of simulations
Tanner, C., White, M. J., Guarino, S., Hall-Craggs, M. A., Douek, M., & Hawkes, D. J. (2011) · Medical Physics · doi:10.1118/1.3525837 · Cited by 32 · cited 2×
“Lastly, maybe the most important choice regards the boundary conditions and whether it is the interaction of compression plates and the breast [207,208,212,214 218], displacement loading [210,219] or both [211,213].”
BackgroundA locally constrained radial basis function for registration and warping of images
Siddiqui, A., Masood, A., & Saleem, M. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.11.004 · Cited by 32 · cited 1×
“[126] defined a new model based on the cosine function.”
MethodGuo, H., Rangarajan, A., & Joshi, S. C. (2005) · Springer eBooks · doi:10.1007/0-387-28831-7_13 · Cited by 31 · cited 1×
“has presented a solution for both the exact and inexact landmark matching problems for the case of diffeomorphic deformations [297].”
BackgroundA hybrid fem-based method for aligning prone and supine images for image guided breast surgery
Han, L., Hipwell, J. H., Mertzanidou, T., Carter, T. J., Marc Modat, A., Sebastien Ourselin, A., & Hawkes, D. J. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872626 · Cited by 31 · cited 1×
“FEMs have been used either to register the images [200], [204] or to provide a more appropriate initialization for standard intensity-based nonrigid registration methods [205]–[207].”
MethodComparison of vesselness functions for multiscale analysis of the liver vasculature
Drechsler, K., & Laura, C. O. (2010) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2010.5687627 · Cited by 31 · cited 1×
“Loeckx et al. [238] locally constrained a non-rigid FFD registration method by penalizing deviations of the Jacobian from orthogonality.”
MethodLarge Diffeomorphic FFD Registration for Motion and Strain Quantification from 3D-US Sequences
Craene, M. D., Cámara, Ó., Bijnens, B. H., & Frangi, A. F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01932-6_47 · Cited by 30 · cited 2×
“Despite being limited with respect to the diffeomorphisms that they can capture, stationary velocity fields are a common choice among many researchers [97]–[100].”
BackgroundA Full Curvature Based Algorithm for Image Registration
Henn, S. (2006) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-005-3621-3 · Cited by 30 · cited 1×
“this fact, the resulting underlying function space penalizes the affine linear displacements as pointed out by Henn in [60].”
BackgroundSimultaneous Geometric - Iconic Registration
Sotiras, A., Ou, Y., Glocker, B., Davatzikos, C. A., & Nikos Paragios, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15745-5_83 · Cited by 29 · cited 2×
“Sotiras et al. used it to perform hybrid registration [148].”
BackgroundMethodSelf-citationRobust Computation of Mutual Information Using Spatially Adaptive Meshes
Sundar, H., Shen, D. G., Biros, G., Xu, C., & Davatzikos, C. A. (2007) · Springer eBooks · doi:10.1007/978-3-540-75757-3_115 · Cited by 29 · cited 1×
“proposed a robust way to compute MI [348].”
MethodSelf-citationf -information measures in medical image registration
Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2001) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.431132 · Cited by 28 · cited 1×
“[336] compared the performance of a number of”
Mammogram registration: a phantom-based evaluation of compressed Breast Thickness variation effects
Richard, F. J. P., Bakić, P. R., & Maidment, A. D. A. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.862204 · Cited by 28
A Hamiltonian Particle Method for Diffeomorphic Image Registration
Marsland, S., & McLachlan, R. I. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73273-0_33 · Cited by 28 · cited 3×
“Marsland and McLachlan [94] formulated the problem in a PDE framework and used a particle method to solve for the diffeomorphism.”
MethodCortical Hemisphere Registration Via Large Deformation Diffeomorphic Metric Curve Mapping
Qiu, A., & Miller, M. I. (2007) · Springer eBooks · doi:10.1007/978-3-540-75757-3_23 · Cited by 27 · cited 1×
“The cortical surface information has also served as feature in [273]–[275].”
BackgroundDiffeomorphic Nonlinear Transformations: A Local Parametric Approach for Image Registration
Narayanan, R., Fessler, J. A., Park, H., & Meyer, C. R. (2005) · Lecture notes in computer science · doi:10.1007/11505730_15 · Cited by 27 · cited 1×
“tion model that is affine at the center of a region and reduces to identity as the distance from the center increases [167].”
Background3D multimodality medical image registration using morphological tools
Maintz, J. B. A., van den Elsen, P. A., & Viergever, M. A. (2001) · Image and Vision Computing · doi:10.1016/s0262-8856(00)00051-2 · Cited by 27 · cited 1×
“[366] tackled rigid multi-modal registration by”
Nowinski, W. L., & Belov, D. I. (2005) · Academic Radiology · doi:10.1016/j.acra.2005.04.018 · Cited by 27 · cited 2×
“Nowinski and Belov [193] refined the result of a Talairach landmark registration by assuming a radial mass-effect tumor model.”
BackgroundBoosted metric learning for 3D multi-modal deformable registration
Michel, F., Bronstein, M. M., Bronstein, A. M., & Nikos Paragios, A. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872619 · Cited by 27 · cited 1×
“Another supervised technique was presented in [303,304] to learn a similarity metric that discerns between corresponding and non-corresponding points.”
MethodNonrigid Image Registration Using Dynamic Higher-Order MRF Model
Kwon, D., Lee, K. J., Yun, I. D., & Lee, S. U. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_29 · Cited by 26 · cited 1×
“[442] used TRW-S to perform inference in a factor graph that models higher-order spatial smoothness constraints for image registration.”
MethodHigh-Dimensional Normalized Mutual Information for Image Registration Using Random Lines
Bardera, A., Feixas, M., Boada, I., & Sbert, M. (2006) · Lecture notes in computer science · doi:10.1007/11784012_32 · Cited by 26 · cited 1×
“[353] investigated NMI between blocks of image elements.”
BackgroundA robust hybrid method for nonrigid image registration
Yang, J., Williams, J. P., Sun, Y., Blum, R. S., & Xu, C. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.10.009 · Cited by 26 · cited 1×
“used salient the scale invariant features [253] to tackle geometric registration that infers both the correspondence and the spatial transformation [268].”
BackgroundFourier Methods for Nonparametric Image Registration
Cahill, N. D., Noble, J. A., & Hawkes, D. J. (2007) · 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.2007.383424 · Cited by 26 · cited 1×
“[34] showed how to use Fourier methods to efficiently solve the linear PDE system that Sotiras et al.”
MethodNon-rigid Image Registration Using a Statistical Spline Deformation Model
Loeckx, D., Maes, F., Vandermeulen, D., & Suetens, P. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45087-0_39 · Cited by 26
Towards Tracking Breast Cancer Across Medical Images Using Subject-Specific Biomechanical Models
Rajagopal, V., Lee, A., Chung, J.-H., Warren, R. M. L., Highnam, R. P., Nielsen, P. M. F., & Nash, M. P. (2007) · Springer eBooks · doi:10.1007/978-3-540-75757-3_79 · Cited by 25 · cited 1×
“FEMs have been used either to register the images [200], [204] or to provide a more appropriate initialization for standard intensity-based nonrigid registration methods [205]–[207].”
MethodDemons algorithms for fluid and curvature registration
Cahill, N. D., Noble, J. A., & Hawkes, D. J. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5193151 · Cited by 25 · cited 2×
“Cahill et al. [48] showed that curvature and fluid registration can be formulated as two coupled diffusion equations.”
BackgroundAUTOMATIC IMAGE MATCHING FOR BREAST CANCER DIAGNOSTICS BY A 3D DEFORMATION MODEL OF THE MAMMA
Ruiter, N. V., Müller, T. O., Stotzka, R., Gemmeke, H., Reichenbach, J. R., & Kaiser, W. A. (2002) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmte.2002.47.s1b.644 · Cited by 24 · cited 1×
“Biomechanical modeling is important in tackling large deformations which are typical in breast imaging applications such as cancer diagnosis [197], surgical planning [198] and image-guided interventions [199].”
BackgroundDistribution of Fiducial Registration Error in Rigid-Body Point-Based Registration
Moghari, M. H., & Abolmaesumi, P. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2024208 · Cited by 24 · cited 1×
“Some resent studies regarding the errors in the process are given in [284]–[286].”
BackgroundRobust Registration and Tracking Using Kernel Density Correlation
Singh, M., Arora, H., & Ahuja, N. (2005) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.433 · Cited by 24 · cited 1×
“presented a similar approach based on kernel density correlation [299].”
MethodAn Interactive Hybrid Non-Rigid Registration Framework for 3D Medical Images
Azar, A. T., Xu, C., Pennec, X., & Ayache, N. (2006) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2006.1625045 · Cited by 24 · cited 1×
“proposed a user-guided registration method [388].”
Maximum distance-gradient for robust image registration
Gan, R., Chung, A. C. S., & Liao, S. (2008) · Medical Image Analysis · doi:10.1016/j.media.2008.01.004 · Cited by 23 · cited 1×
“also employed a multi-dimensional NMI criterion [356].”
Learning similarity measure for multi-modal 3D image registration
Lee, D., Hofmann, M., Steinke, F., Altün, Y., Cahill, N. D., & Schölkopf, B. (2009) · 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/cvprw.2009.5206840 · Cited by 23 · cited 1×
“presented a supervised technique to learn the similarity measure for multi-modal image registration [375].”
MethodGraphical Models and Deformable Diffeomorphic Population Registration Using Global and Local Metrics
Sotiras, A., Nikos Komodakis, A., Glocker, B., Deux, J., & Nikos Paragios, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_83 · Cited by 23 · cited 1×
“used this optimizer to tackle group-wise registration [438], [439].”
MethodSelf-citationMulti-level non-rigid image registration using graph-cuts
So, R. W. K., & Chung, A. C. S. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959604 · Cited by 22 · cited 1×
“In medical image registration, α -expansion is the optimizer used by Tang and Chung [419], So et al. [420]–[422] and Liao and Chung [326].”
MethodApproximated Curvature Penalty in Non-rigid Registration Using Pairwise MRFs
Glocker, B., Nikos Komodakis, A., Nikos Paragios, A., & Navab, N. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10331-5_102 · Cited by 22 · cited 1×
“[61] used an approximation of the curvature penalty in the case of parametric grid-based deformation”
MethodRobust Nonrigid Multimodal Image Registration Using Local Frequency Maps
Jian, B., Vemuri, B. C., & Marroquin, J. L. (2005) · Lecture notes in computer science · doi:10.1007/11505730_42 · Cited by 21 · cited 1×
“Jian et al. used local frequency maps for deformable registration [371].”
BackgroundRevisiting overlap invariance in medical image alignment
Cahill, N. D., Schnabel, J. A., Noble, J. A., & Hawkes, D. J. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4562989 · Cited by 21 · cited 1×
“elaborated upon the idea of overlap invariance and showed that neither NMI, MI, CR, CCor, nor CCoef are invariant to changes of overlap and proposed appropriate invariant versions of the previous similarity measures in [334].”
BackgroundUnbiased Volumetric Registration via Nonlinear Elastic Regularization
Yanovsky, I., Guyader, C. L., Leow, A., Toga, A. W., Thompson, P. M., & Vese, L. A. (2008) · HAL · Cited by 21
A Framework for Brain Registration via Simultaneous Surface and Volume Flow
Joshi, A. A., Leahy, R. M., Toga, A. W., & Shattuck, D. W. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02498-6_48 · Cited by 20
Graph-Based Geometric-Iconic Guide-Wire Tracking
Honnorat, N., Vaillant, R., & Nikos Paragios, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23623-5_2 · Cited by 20 · cited 1×
“[395] used the exact L2 distance to couple the geometric and iconic information for the problem of guide-wire tracking.”
Nonrigid 3D Brain Registration Using Intensity/Feature Information
DeLorenzo, C., Papademetris, X., Wu, K., Vives, K. P., Spencer, D. D., & Duncan, J. S. (2006) · Lecture notes in computer science · doi:10.1007/11866565_114 · Cited by 19 · cited 1×
“Registration between preoperative and intra-operative images has also been attempted with hybrid approaches [361].”
BackgroundNon-rigid brain image registration using a statistical deformation model
Wouters, J., D’Agostino, E., Maes, F., Vandermeulen, D., & Suetens, P. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.653081 · Cited by 19 · cited 1×
“[171] used PCA to model the deformation and registration was performed by adjusting the coefficients of the principal components while maximizing mutual information (MI).”
MethodMulti-channel Mutual Information Using Scale Space
Holden, M. P., Griffin, L. D., Saeed, N., & Hill, D. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30135-6_97 · Cited by 18 · cited 1×
“[355] employed Gaussian scale space derivatives and incorporated them as an additional information channel in a higher dimensional MI criterion.”
MethodDiscrete symmetric image registration
Sotiras, A., & Nikos Paragios, A. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235554 · Cited by 18 · cited 6×
“N. Paragios is with the Center for Visual Computing, Department of Applied Mathematics, Ecole Centrale de Paris, Chatenay-Malabry, 92 295 FRANCE, the Equipe Galen, INRIA Saclay - Ile-de-France, Orsay, 91893 FRANCE and the Universite Paris-Est, LIGM (UMR CNRS), Center for Visual Computing, Ecole des Ponts ParisTech, Ch…”
BackgroundMethodInfluentialSelf-citationA hybrid multimodal non-rigid registration of MR images based on diffeomorphic demons
Lu, H., Cattin, P. C., & Reyes, M. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627562 · Cited by 18 · cited 1×
“[391] incorporated landmark information in the diffeomorphic Demons registration algorithm [99].”
MethodNon-rigid image registration by using graph-cuts with mutual information
So, R. W. K., & Chung, A. C. S. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5653265 · Cited by 18
A Nonconservative Lagrangian Framework for Statistical Fluid Registration—SAFIRA
Brun, C. C., Leporé, N., Pennec, X., Chou, Y., Lee, A. D., de Zubicaray, G. I., McMahon, K. L., Wright, M. J., Gee, J. C., & Thompson, P. M. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2067451 · Cited by 18 · cited 1×
“This framework was further developed in [177] by computing statistical priors on both the deformation tensors and the displacement vector elds in a nonconservative Lagrangian uid registration algorithm.”
MethodAutomatic hybrid registration for 2-dimensional CT abdominal images
Wang, X.-Y., & Feng, D. (2004) · International Conference on Image and Graphics · doi:10.1109/icig.2004.42 · Cited by 17 · cited 1×
“Similar methods were also proposed in the case of abdominal CT [354] and retinal [355] images.”
MethodA New Divergence Measure for Medical Image Registration
Martin, S., & Durrani, T. S. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.891772 · Cited by 17 · cited 1×
“Martin and Durrani introduced a generalization of KLD in [344].”
BackgroundImage registration using entropic graph-matching criteria
Neemuchwala, H., Hero, A. O., & Carson, P. L. (2003) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2002.1197163 · Cited by 17 · cited 1×
“Neemuchwala et al. [342] used a Minimum Spanning Tree (MST) to estimate the RE.”
MethodBiesdorf, A., Wörz, S., Kaiser, H.-J., Stippich, C., & Rohr, K. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_75 · Cited by 16 · cited 1×
“A similar approach was presented in [363].”
Symmetric Nonrigid Image Registration: Application to Average Brain Templates Construction
Noblet, V., Heinrich, C., Heitz, F., & Armspach, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_108 · Cited by 16 · cited 4×
“Noblet et al. extended this approach to the 3D domain in [158] and further validated it in [159].”
BackgroundInfluentialSingular solutions, momentum maps and computational anatomyPreprint
Cotter, C. J., & Holm, D. D. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.nlin/0605020 · Cited by 16
Nonrigid Brain MR Image Registration Using Uniform Spherical Region Descriptor
Liao, S., & Chung, A. C. S. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2159615 · Cited by 16 · cited 2×
“Hybrid methods combine both types of information in an effort to get the best of both worlds.”
MethodLandmark/image-based deformable registration of gene expression data
Kurkure, U., Le, Y. H., Nikos Paragios, A., Carson, J. P., Ju, T., & Kakadiaris, I. A. (2011) · doi:10.1109/cvpr.2011.5995708 · Cited by 16 · cited 1×
“[396] used learned higher-order potential for the layer of the graph that models the geometric problem.”
MethodOn affine registration of planar point sets using complex numbers
Ho, J. C. F., & Yang, M. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.07.007 · Cited by 16 · cited 1×
“Recently, an algebraic method based on complex numbers, to solve for the a ne registration of planar sets, was presented in [310].”
MethodDeformable Density Matching for 3D Non-rigid Registration of Shapes
Roy, A. S., Gopinath, A., & Rangarajan, A. (2007) · Springer eBooks · doi:10.1007/978-3-540-75757-3_114 · Cited by 15 · cited 1×
“[319] modeled each feature of each shape as GMM.”
MethodHybrid feature-based Log-Demons registration for tumour tracking in 2-D liver ultrasound images
Cifor, A., Risser, L., Chung, D. Y. F., Anderson, E. M., & Schnabel, J. A. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235650 · Cited by 15 · cited 1×
“[399] also extended [47] to take into account geometric”
BackgroundLearning-based deformation estimation for fast non-rigid registration
Kim, M., Kim, M.-H., & Shen, D. G. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563006 · Cited by 15 · cited 1×
“In [169,170] the intermediate template was predicted by support vector regression models that had learned the correlations between deformations and image appearances.”
MethodGeneralized L2-Divergence and Its Application to Shape Alignment
Wang, F., Vemuri, B. C., & Syeda-Mahmood, T. F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02498-6_19 · Cited by 14 · cited 1×
“[321] alleviated this shortcoming by using the generalized L2-divergence that allows for a closed-form solution.”
BackgroundMultimodal Registration via Spatial-Context Mutual Information
Zhao, Y., & Soatto, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22092-0_35 · Cited by 14 · cited 2×
“Recently, a similar approach was presented by Yi and Soatto [282].”
ResultRegularized methods for topology-preserving smooth nonrigid image registration using B-spline basis
Chun, S. Y., & Fessler, J. A. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541192 · Cited by 13 · cited 3×
“Chun and Fessler devised a simpler penalty for the case of B-splines [231].”
MethodInfluentialA general preconditioning scheme for difference measures in deformable registration
Zikic, D., Baust, M., Kamen, A., & Navab, N. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126224 · Cited by 13 · cited 1×
“[376] proposed a preconditioning scheme that improves the convergence speed of registration algorithms.”
BackgroundBrain warping with implicit representations
Leow, A., Thompson, P. M., Protas, H. D., & Huang, S. (2005) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2004.1398610 · Cited by 13 · cited 2×
“used implicit representation to tackle brain warping [83], [85].”
BackgroundMethodLearning-Based 2D/3D Rigid Registration Using Jensen-Shannon Divergence for Image-Guided Surgery
Liao, R., Guetter, C., Xu, C., Sun, Y., Khamene, A., & Sauer, F. (2006) · Lecture notes in computer science · doi:10.1007/11812715_29 · Cited by 12 · cited 2×
“Then, either a maximum likelihood approach [378] or a divergence criterion [337]–[339], [343] is used to compare the estimated and learned distributions.”
MethodDetecting mutually-salient landmark pairs with MRF regularization
Ou, Y., Besbes, A., Bilello, M., Mansour, M., Davatzikos, C. A., & Nikos Paragios, A. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490324 · Cited by 12 · cited 1×
“In medical imaging, [329] recently presented a method to detect mutually-salient pairs in brain images.”
MethodSelf-citationRegistration Using Segment Intensity Remapping and Mutual Information
Knops, Z. F., Maintz, J. B. A., Viergever, M. A., & Pluim, J. P. W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30135-6_98 · Cited by 12 · cited 1×
“[289] performed a k-means clustering before registration.”
Image transport regression using mixture of experts and discrete Markov Random Fields
Michel, F., & Nikos Paragios, A. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490217 · Cited by 12 · cited 1×
“Michel and Paragios [365] used the mixture of experts methods to learn the conditional probability of the target intensity given a source patch.”
MethodComputing the Geodesic Interpolating Spline
Mills, A., Shardlow, T., & Marsland, S. (2006) · Lecture notes in computer science · doi:10.1007/11784012_21 · Cited by 12 · cited 1×
“Younes extended this method to combine GIS with affine transformations in [114] while two ways to calculate them were presented by Mills et al. [115].”
MethodHybrid Spline-Based Elastic Image Registration Using Analytic Solutions of the Navier Equation
Wörz, S., & Rohr, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-71091-2_31 · Cited by 11 · cited 1×
“A dense deformation eld was created in [362] by using both landmark correspondences and their localization uncertainties.”
MethodRajagopal, V., Chung, J.-H., Highnam, R. P., Warren, R. M. L., Nielsen, P. M. F., & Nash, M. P. (2010) · Springer eBooks · doi:10.1007/978-1-4419-5874-7_3 · Cited by 10 · cited 1×
“Other modeling choices include the inclusion of friction between breast and plates in the contact model [209, 212, 218] or do not (frictionless) [208, 211, 214, 215].”
BackgroundMammography Registered Tactile Imaging
Galea, A. M., & Howe, R. D. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45015-7_18 · Cited by 10
Gibson, E., KHAN, A. R., & Beg, M. F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_88 · Cited by 10 · cited 1×
“Recently, a similar approach was presented in [352].”
ResultAn extension of thin-plate splines for image registration with radial basis functions
Rohr, K., & Wörz, S. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235579 · Cited by 10 · cited 2×
“Rohr andWörz [109] introduced a variant of TPS which assumes that the forces that act at the landmarks, also”
BackgroundDeformable 3D Volume Registration Using Efficient MRFs Model with Decomposed Nodes
Lee, K. J., Kwon, D., Yun, I. D., & Lee, S. U. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.59 · Cited by 10 · cited 1×
“This model was extended to the 3D case in [406].”
MethodRecursive Green’s Function Registration
Beuthien, B., Kamen, A., & Fischer, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15745-5_67 · Cited by 9 · cited 1×
“[62], inspired by the approach presented in [32] for the viscous fluid”
MethodLocal similarity measures for demons-like registration algorithms
Tristán‐Vega, A., Vegas‐Sánchez‐Ferrero, G., & Aja‐Fernández, S. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541189 · Cited by 9 · cited 1×
“Other efforts include the encoding of similarity metrics such as Normalized Mutual Information by Trist´an-Vega et al. [53] and Modat et al. [54].”
MethodCombining geodesic interpolating splines and affine transformations
Laurent Younes, B. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.864163 · Cited by 9 · cited 1×
“An extension to combine them with a ne transformations was given in [115] while two ways to calculate them were presented by Mills et al.”
MethodCOMPACT SUPPORT THIN PLATE SPLINE ALGORITHM
.., L., Jing, J., Yang, Xuan, X., Yu, & Jianping (2007) · Cited by 9
Locally constrained cubic B-spline deformations to control volume variations
Dauguet, J., Hérard, A., Declerck, J. M., & Delzescaux, T. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5193219 · Cited by 8 · cited 2×
“Another way to solve the problem is by augmenting the dimensionality of the problem using the method of Lagrange multipliers [235], [237].”
MethodA new consistent image registration formulation with a B-spline deformation model
Feng, W., Reeves, S. J., Denney, T. S., Lloyd, S. G., Dell’Italia, L. J., & Gupta, H. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5193218 · Cited by 8 · cited 1×
“[144] proposed an inverse consistent method based on FFDs.”
MethodSTATISTICALLY-CONSTRAINED DEFORMABLE REGISTRATION OF MR BRAIN IMAGES
Xue, Z., & Shen, D. G. (2007) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2007.356779 · Cited by 8 · cited 1×
“In a subsequent work [176] conventional registration followed the statistically-constrained registration to re ne the result.”
MethodJoint T1 and Brain Fiber Diffeomorphic Registration Using the Demons
Siless, V., Guevara, P., Pennec, X., & Fillard, P. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24446-9_2 · Cited by 7 · cited 1×
“Siless et al. [397] proposed a coupled approach based on the diffeomorphic Demons algorithm [99].”
MethodEfficient parallel message computation for MAP inference
Alchatzidis, S., Sotiras, A., & Nikos Paragios, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126392 · Cited by 7
Self-citationNon-rigid image registration through efficient discrete optimization.
Heinrich, M. P., Jenkinson, M., Brady, J. M., & Schnabel, J. A. (2011) · MIUA · Cited by 7
Nonrigid free-form registration using landmark-based statistical deformation models
Pszczółkowski, S., Pizarro, L., Guerrero, R., & Rueckert, D. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911441 · Cited by 6 · cited 1×
“demonstrated that the model in [174] can encode landmark position information [176].”
BackgroundAtlas-based deformable mutual population segmentation
Sotiras, A., Nikos Komodakis, A., Langs, G., & Nikos Paragios, A. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5192969 · Cited by 6 · cited 1×
“Sotiras et al. used this optimizer to tackle group-wise registration [438], [439].”
MethodSelf-citationCompact support Thin Plate Spline algorithm
Li, J., Yang, X., & Yu, J. (2007) · Journal of Electronics (China) · doi:10.1007/s11767-005-0236-1 · Cited by 5 · cited 3×
“Furthermore, Cahill et al. extended their analysis to show how these methods can be applied in the case of other regularizers (diffusion, curvature and elastic) under Dirichlet, Neumann or periodic boundary conditions.”
BackgroundMethodInfluentialIconic Feature Registration with Sparse Wavelet Coefficients
Cathier, P. (2006) · Lecture notes in computer science · doi:10.1007/11866763_85 · Cited by 4 · cited 1×
“Cathier [160] used the same wavelet basis as in [155] to decompose the transformation in a multi-resolution fashion.”
MethodMotion-based enhancement of optical imaging
Savinaud, M., Nikos Paragios, A., & Maitrejean, S. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5193153 · Cited by 4 · cited 1×
“represented both silhouettes and landmarks using the Euclidean distance transform [308].”
MethodA Kernel-based graphical model for diffusion tensor registration
Sotiras, A., Neji, R., Deux, J.-F., Nikos Komodakis, A., Fleury, G. A., & Nikos Paragios, A. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490295 · Cited by 4 · cited 1×
“[415] to solve di usion tensor registration.”
Self-citationBioluminescence enhancement through fusion of optical imaging and cinematic video flow
Savinaud, M., Sotiras, A., Maitrejean, S., & Nikos Paragios, A. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490081 · Cited by 3 · cited 1×
“A decomposed model for group-wise registration of both single- and multi-channel images was presented in [416,417].”
MethodSelf-citationJoint T1 and brain fiber diffeomorphic registration using the demons
Siless, V., Guevara, P., Pennec, X., Thirion, B., & Fillard, P. (2012) · HAL · Cited by 3
Comments on: A locally constrained radial basis function for registration and warping of images
Tristán‐Vega, A., & García-Pérez, V. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.11.012 · Cited by 2 · cited 1×
“Contrary to what is claimed in the paper, the new model is not positive definite [127].”
ResultElastic image deformation using adaptive support radial basic function
Yang, X., Zhang, Z., & Pei, J. (2008) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2008.4635768 · Cited by 2 · cited 2×
“These approaches [107], [108] were based on heuristics”
BackgroundMethod
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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Cited by
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).”
BackgroundVoxelMorph: 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].”
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
“on correction, real-time MR physics is also an important part of the picture [224, 225]. A wide range of methodological approaches have been developed and tested for various organs and applications40 [229, 230, 231, 232, 233, 234, 235, 236, 237, 238], including \previous generation" articial neural networks [239]…”
MethodInfluentialInfrared and visible image fusion methods and applications: A survey
Ma, J., Ma, Y., & Li, C. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.02.004 · Cited by 1,800
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J.-Y., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 977
“Please refer to Zitova and Flusser (2003), Dawn et al. (2010), Sotiras et al. (2013) and Ferrante and Paragios (2017) for representative literature and further details.”
Background
Metadata
- Authors
- Sotiras, A., Davatzikos, C., & Paragios, N.
- Affiliations
- University of Pennsylvania (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-07
- Volume
- 32
- Issue
- 7
- Pages
- 1153-1190
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- 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
- 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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