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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

Different Medical Image Registration Techniques: A Comparative Analysis

Author(s): Suyambu Karthick* and S. Maniraj

Volume 15, Issue 10, 2019

Page: [911 - 921] Pages: 11

DOI: 10.2174/1573405614666180905094032

Price: $65

Abstract

Background: Image registration provides major role in real world applications and classic digital image processing. Image registration is carried out for more than one image and this image was captured from a different location, different sensors, different time and different viewpoints.

Discussion: This paper deals with the comparative analysis of various registration techniques and here six registration techniques depending upon intensity, phase correlation, image feature, area, control points and mutual information are compared. Comparative analysis for different methodologies shows the advantages of one method over the other methods. The foremost objective of this paper is to deliver a complete reference source for the scholars interested in registration, irrespective of specific application extents.

Conclusion: Finally performance analyses are evaluated for the medical datasets and comparison is graphically shown with the MATLAB simulation tool.

Keywords: Image registration, transformation, feature detection, similarity measure, optimization, MATLAB.

Graphical Abstract

[1]
Myronenko A, Song X. Intensity-based image registration by minimizing residual complexity. IEEE Trans Med Imaging 2010; 29(11): 1882-91.
[http://dx.doi.org/10.1109/TMI.2010.2053043] [PMID: 20562036]
[2]
Rohde GK, Aldroubi A, Dawant BM. The adaptive bases algorithm for intensity-based nonrigid image registration. IEEE Trans Med Imaging 2003; 22(11): 1470-9.
[http://dx.doi.org/10.1109/TMI.2003.819299] [PMID: 14606680]
[3]
Perumal SS, Vishwanath N, Jer Lang H, Karthick S. An effective content based medical image retrieval by using ABC based Artificial Neural Network (ANN). Curr Med Imaging 2017; 13(3): 223-30.
[4]
Mori K, Deguchi D, Sugiyama J, et al. Tracking of a bronchoscope using epipolar geometry analysis and intensity-based image registration of real and virtual endoscopic images. Med Image Anal 2002; 6(3): 321-36.
[http://dx.doi.org/10.1016/S1361-8415(02)00089-0] [PMID: 12270236]
[5]
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002; 17(2): 825-41.
[http://dx.doi.org/10.1006/nimg.2002.1132] [PMID: 12377157]
[6]
Oliveira FPM, Tavares JM. Medical image registration: a review. Comput Methods Biomech Biomed Engin 2014; 17(2): 73-93.
[http://dx.doi.org/10.1080/10255842.2012.670855] [PMID: 22435355]
[7]
Clippe S, Sarrut D, Malet C, Miguet S, Ginestet C, Carrie C. Patient setup error measurement using 3D intensity-based image registration techniques. Int J Radiat Oncol Biol Phys 2003; 56(1): 259-65.
[http://dx.doi.org/10.1016/S0360-3016(03)00083-X] [PMID: 12694847]
[8]
Xing C, Qiu P. Intensity-based image registration by nonparametric local smoothing. IEEE Trans Pattern Anal Mach Intell 2011; 33(10): 2081-92.
[http://dx.doi.org/10.1109/TPAMI.2011.26] [PMID: 21321367]
[9]
Zikic D, Glocker B, Kutter O, et al. Linear intensity-based image registration by Markov random fields and discrete optimization. Med Image Anal 2010; 14(4): 550-62.
[http://dx.doi.org/10.1016/j.media.2010.04.003] [PMID: 20537936]
[10]
Bhagalia R, Fessler JA, Kim B. Accelerated nonrigid intensity-based image registration using importance sampling. IEEE Trans Med Imaging 2009; 28(8): 1208-16.
[http://dx.doi.org/10.1109/TMI.2009.2013136] [PMID: 19211343]
[11]
Chen HM, Varshney PK. Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation. IEEE Trans Med Imaging 2003; 22(9): 1111-9.
[http://dx.doi.org/10.1109/TMI.2003.816949] [PMID: 12956266]
[12]
Zitova B, Flusser J. Image registration methods: a survey. Image Vis Comput 2003; 21(11): 977-1000.
[http://dx.doi.org/10.1016/S0262-8856(03)00137-9]
[13]
Yong SK, Lee JH, Ra JB. Multi-sensor image registration based on intensity and edge orientation information. Pattern Recognit 2003; 41(11): 3356-65.
[14]
Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 2009; 48(1): 63-72.
[http://dx.doi.org/10.1016/j.neuroimage.2009.06.060] [PMID: 19573611]
[15]
Russakoff DB, Rohlfing T, Ho A, et al. Evaluation of intensity-based 2D-3D spine image registration using clinical gold-standard data. Proceedings of the International workshop on Biomedical Image Registration. 2003 June 23-24; Philadelphia, PA, USA Springer 2003..
[http://dx.doi.org/10.1007/978-3-540-39701-4_16]
[16]
Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001; 5(2): 143-56.
[http://dx.doi.org/10.1016/S1361-8415(01)00036-6] [PMID: 11516708]
[17]
Wachowiak MP, Smolíková R, Zheng Y, Zurada JM, Elmaghraby AS. An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans Evol Comput 2004; 8(3): 289-301.
[http://dx.doi.org/10.1109/TEVC.2004.826068]
[18]
Bodduluri S, Newell JD Jr, Hoffman EA, Reinhardt JM. Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework. Acad Radiol 2013; 20(5): 527-36.
[http://dx.doi.org/10.1016/j.acra.2013.01.019] [PMID: 23570934]
[19]
Fischer B, Modersitzki J. Curvature based image registration. J Math Imaging Vis 2003; 18(1): 81-5.
[http://dx.doi.org/10.1023/A:1021897212261]
[20]
Díez Y, Oliver A, Lladó X, et al. Revisiting intensity-based image registration applied to mammography. IEEE Trans Inf Technol Biomed 2011; 15(5): 716-25.
[http://dx.doi.org/10.1109/TITB.2011.2151199] [PMID: 21550890]
[21]
Rohlfing T, Maurer CR Jr. Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans Inf Technol Biomed 2003; 7(1): 16-25.
[http://dx.doi.org/10.1109/TITB.2003.808506] [PMID: 12670015]
[22]
Hopp T, Dietzel M, Baltzer PA, et al. Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization. Med Image Anal 2013; 17(2): 209-18.
[http://dx.doi.org/10.1016/j.media.2012.10.003] [PMID: 23265802]
[23]
Viergever MA, Maintz JBA, Klein S, Murphy K, Staring M, Pluim JPW. A survey of medical image registration-under review. Med Image Anal 2016; 33: 140-4.
[24]
Li Y, Wang S, Tian Q, Ding X. A survey of recent advances in visual feature detection. Neurocomputing 2015; 149(1): 736-51.
[http://dx.doi.org/10.1016/j.neucom.2014.08.003]
[25]
Hassaballah M. Aly Amin Abdelmgeid, Hammam A Alshazly Image features detection, description and matching Image Feature Detectors and Descriptors. Berlin: Springer 2016; pp. 11-45.
[http://dx.doi.org/10.1007/978-3-319-28854-3_2]
[26]
Remondino F, Spera MG, Nocerino E, Menna F, Nex F. State of the art in high density image matching. Photogramm Rec 2014; 29(146): 144-66.
[http://dx.doi.org/10.1111/phor.12063]
[27]
Vergara JR, Estévez PA. A review of feature selection methods based on mutual information. Neural Comput Appl 2014; 24(1): 175-86.
[http://dx.doi.org/10.1007/s00521-013-1368-0]
[28]
Denton EL, Chintala S, Fergus R. Deep generative image models using a laplacian pyramid of adversarial networks. Comp Vis Patt Recogn 2015; 2015: 1486-94.
[29]
Rokni K, Ahmad A, Solaimani K, Hazini S. A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. Int J Appl Earth Obs Geoinf 2015; 34(1): 226-34.
[http://dx.doi.org/10.1016/j.jag.2014.08.014]
[30]
Cheng M-M, Mitra NJ, Huang X, Torr PH, Hu SM. Torr, Shi-Min Hu. Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 2015; 37(3): 569-82.
[http://dx.doi.org/10.1109/TPAMI.2014.2345401] [PMID: 26353262]
[31]
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 2018; 40(4): 834-48.
[http://dx.doi.org/10.1109/TPAMI.2017.2699184] [PMID: 28463186]
[32]
Yang K, Pan A, Yang Y, Zhang S, Ong SH, Tang H. Remote sensing image registration using multiple image features. Remote Sens 2017; 9(6): 581.
[http://dx.doi.org/10.3390/rs9060581]
[33]
Shun Miao, Wang ZJ, Rui Liao. A CNN regression approach for real-time 2D/3D registration. IEEE Trans Med Imaging 2016; 35(5): 1352-63.
[http://dx.doi.org/10.1109/TMI.2016.2521800] [PMID: 26829785]
[34]
Brock KK, Mutic S, McNutt TR, Li H, Kessler ML. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM radiation therapy committee task group no. 132. Med Phys 2017; 44(7): e43-76.
[http://dx.doi.org/10.1002/mp.12256] [PMID: 28376237]
[35]
Ma J, Zhou H, Zhao J, Gao Y, Jiang J, Tian J. Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Trans Geosci Remote Sens 2015; 53(12): 6469-81.
[http://dx.doi.org/10.1109/TGRS.2015.2441954]
[36]
Bak A, Bouchafa S, Aubert D. Dynamic objects detection through visual odometry and stereo-vision: a study of inaccuracy and improvement sources. Mach Vis Appl 2014; 25(3): 681-97.
[http://dx.doi.org/10.1007/s00138-011-0389-x]
[37]
Lemon WC, Pulver SR, Höckendorf B, et al. Whole-central nervous system functional imaging in larval Drosophila. Nat Commun 2015; 6(1): 7924.
[http://dx.doi.org/10.1038/ncomms8924] [PMID: 26263051]
[38]
Webster JD, Dunstan RW. Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology. Vet Pathol 2014; 51(1): 211-23.
[http://dx.doi.org/10.1177/0300985813503570] [PMID: 24091812]
[39]
Tuba E, Tuba M, Dolicanin E. Adjusted fireworks algorithm applied to retinal image registration. Stud Inform Control 2017; 26(1): 33-42.
[http://dx.doi.org/10.24846/v26i1y201704]
[40]
Karthick S. Semi supervised hierarchy forest clustering and KNN based metric learning technique for machine learning system. J Adv Res Dyn Control Syst 2017; 9(18): 2679-90.
[41]
de Vos BD, Berendsen FF, Viergever MA, Staring M, Išgum I. End-to-end unsupervised deformable image registration with a convolutional neural network Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Berlin: Springer 2017; pp. 204-12.
[http://dx.doi.org/10.1007/978-3-319-67558-9_24]
[42]
Rui L, Miao S, de Tournemire P, et al. An artificial agent for robust image registration. Comp Vis Patt Recogn 2017; 2017: 4168-75.
[43]
Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017; 19(1): 221-48.
[http://dx.doi.org/10.1146/annurev-bioeng-071516-044442] [PMID: 28301734]
[44]
Parmehr EG, Fraser CS, Zhang C. Automatic parameter selection for intensity-based registration of imagery to LiDAR data. IEEE Trans Geosci Remote Sens 2016; 54(12): 7032-43.
[http://dx.doi.org/10.1109/TGRS.2016.2594294]
[45]
Fan S-KS, Chuang Y-C. An entropy-based image registration method using image intensity difference on overlapped region. Mach Vision Appl 2012; 23(4): 791-804.
[46]
Miao S, Huynh T, Adnet C, Pfister M, Rui L. Intensity-based 3D-2D mesh-to-image registration using mesh-based digitally reconstructed radiography. In: Liao H, Linte CA, Masamune K, Peters TM, Zheng G, Eds. Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions. Berlin, Heidelberg: Springer 2013; pp. 86-96.
[47]
Wörz S, Rohr K. Spline-based hybrid image registration using landmark and intensity information based on matrix-valued non-radial basis functions. Int J Comput Vis 2014; 106(1): 76-92.
[http://dx.doi.org/10.1007/s11263-013-0642-z]
[48]
Song H, Qiu P. A parametric intensity-based 3D image registration method for magnetic resonance imaging. Signal Image Video Process 2017; 11(3): 455-62.
[http://dx.doi.org/10.1007/s11760-016-0981-7]
[49]
Yu D, Yang F, Yang C, et al. Fast rotation-free feature-based image registration using improved N-SIFT and GMM-based parallel optimization. IEEE Trans Biomed Eng 2016; 63(8): 1653-64.
[http://dx.doi.org/10.1109/TBME.2015.2465855] [PMID: 26259212]
[50]
Ma J, Zhou H, Zhao J, Gao Y, Jiang J, Tian J. Robust feature matching for Remote Sens. image registration via locally linear transforming. IEEE Trans Geosci Remote Sens 2015; 53(12): 6469-81.
[http://dx.doi.org/10.1109/TGRS.2015.2441954]
[51]
Wan F, Deng F. An image registration method based on feature matching Advanced Research on Computer Education, Simulation and Modeling. Berlin, Heidelberg: Springer 2011; pp. 91-5.
[http://dx.doi.org/10.1007/978-3-642-21802-6_15]
[52]
Mahmoud H, Masulli F, Rovetta S. Feature-based medical image registration using a fuzzy clustering segmentation approach. Computational Intelligence Methods for Bioinformatics and Biostatistics CIBB. Lecture Notes in Computer Science. Springer 2012; pp. 37-47.
[53]
Karthick S, Perumal SS, Raja PT. An approach for image encryption/decryption based on quaternion fourier transform. Proceedings of 2018 IEEE International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR). 2018 July 11-13; Ernakulam, India IEEE 2018.
[54]
Pradhan S, Patra D. Enhanced mutual information based medical image registration. IET Image Process 2016; 10(5): 418-27.
[http://dx.doi.org/10.1049/iet-ipr.2015.0346]
[55]
Gong M, Zhao S, Jiao L, Tian D, Wang S. A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information. IEEE Trans Geosci Remote Sens 2014; 52(7): 4328-38.
[http://dx.doi.org/10.1109/TGRS.2013.2281391]
[56]
Xu L, Wan JW, Bian T. A continuous method for reducing interpolation artifacts in mutual information-based rigid image registration. IEEE Trans Image Process 2013; 22(8): 2995-3007.
[http://dx.doi.org/10.1109/TIP.2013.2251644] [PMID: 23481853]
[57]
Rivaz H, Karimaghaloo Z, Collins DL. Self-similarity weighted mutual information: a new nonrigid image registration metric. Med Image Anal 2014; 18(2): 343-58.
[http://dx.doi.org/10.1016/j.media.2013.12.003] [PMID: 24412710]
[58]
Caron G, Dame A, Marchand E. Direct model based visual tracking and pose estimation using mutual information. Image Vis Comput 2014; 32(1): 54-63.
[http://dx.doi.org/10.1016/j.imavis.2013.10.007]
[59]
Sobolewski T, Messer N, Lutz A, et al. Contourlet image preprocessing for enhanced control point selection in airborne image registration. Proceedings of Applied Imagery Pattern Recognit Workshop (AIPR). 2015 Oct 13-15; Washington, DC, USA IEEE 2015 pp. 1-6..
[http://dx.doi.org/10.1109/AIPR.2015.7444529]
[60]
Matjelo N, Jubert FN, Muller N. In: Elleithy K, Sobh T, Ed. New trends in networking. computing, e-learning, systems sciences, and engineering. Berlin: Springer 2015; pp. 117-24.
[http://dx.doi.org/10.1007/978-3-319-06764-3_15]
[61]
Tan C, Zhang Z, Yang X, Yi J. Cardiac image registration by combining point set matching with surface structure features. Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2016 Dec 15-18; Shenzhen, China IEEE 2017;. 395-402.
[62]
Kolesov I, Lee J, Sharp G, Vela P, Tannenbaum A. A stochastic approach to diffeomorphic point set registration with landmark constraints. IEEE Trans Pattern Anal Mach Intell 2016; 38(2): 238-51.
[http://dx.doi.org/10.1109/TPAMI.2015.2448102] [PMID: 26761731]
[63]
Cohen EAK, Kim D, Ober RJ. Cramér-rao lower bound for point based image registration with heteroscedastic error model for application in single molecule microscopy. IEEE Trans Med Imaging 2015; 34(12): 2632-44.
[http://dx.doi.org/10.1109/TMI.2015.2451513] [PMID: 26641728]
[64]
Nath Nishant. Das Anisha. A novel approach to area based image registration in medical imaging. Int J Sci Eng Res 2013; 4(5): 1383-5.
[65]
Lee J, Paik J. Uniform depth region-based registration between colour channels and its application to single camera-based multifocusing. IET Image Process 2013; 7(1): 50-60.
[http://dx.doi.org/10.1049/iet-ipr.2011.0171]
[66]
Gonçalves H, Gonçalves JA, Corte-Real L. HAIRIS: a method for automatic image registration through histogram-based image segmentation. IEEE Trans Image Process 2011; 20(3): 776-89.
[http://dx.doi.org/10.1109/TIP.2010.2076298] [PMID: 20840895]
[67]
Bowen F, Du E, Hu J. New region feature descriptor-based image registration method. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2012 Oct 14-17; Seoul, South Korea: IEEE 2489-94.
[http://dx.doi.org/10.1109/ICSMC.2012.6378118]
[68]
Tian YLJZ, Zhao W. A new affine invariant feature extraction method for SAR image registration. Int J Remote Sens 2014; 35(20): 7219-29.
[http://dx.doi.org/10.1080/01431161.2014.967887]
[69]
Yin Y. MDCT-based dynamic, subject-specific lung models via image registration for CFD-based interrogation of regional lung function. The University of Iowa 2011; pp. 1-140.
[http://dx.doi.org/10.17077/etd.godzrl4o]
[70]
Alba A, Aguilar-Ponce RM, Vigueras-Gómez JF, Arce-Santana E. Phase correlation based image alignment with subpixel accuracy. Proceedings of Mexican International Conference on Artificial Intelligence. 2012 Oct 27- Nov 4; San Luis Potosí, Mexico.
[71]
Li Z, Mahapatra D, Tielbeek JA, Stoker J, van Vliet LJ, Vos FM. Tielbeek, Jaap Stoker, Lucas J. van Vliet, and Frans M. Vos. Image registration based on autocorrelation of local structure. IEEE Trans Med Imaging 2016; 35(1): 63-75.
[http://dx.doi.org/10.1109/TMI.2015.2455416] [PMID: 26186771]
[72]
Li S, Liang X. Research of sub-pixel image registration based on local-phase correlation. Proceedings of the International Conference on Information Engineering and Applications (IEA). 2012 Oct 26-28; Chongqing, China. 2012;
[http://dx.doi.org/10.1007/978-1-4471-4850-0_64]
[73]
Klimaszewski J, Kondej M, Kawecki M, Putz B. Registration of infrared and visible images based on edge extraction and phase correlation approaches Image Processing and Communications Challenges. Berlin, Heidelberg: Springer 2013; pp. 153-62.
[http://dx.doi.org/10.1007/978-3-642-32384-3_19]
[74]
Li Z, Kurihara T, Matsuzaki K, Irie T. Evaluation of medical image registration by using 3D SIFT and phase-only correlation. Proceedings of 4th International Workshop, Held in Conjunction with MICCAI 2012 Oct 1. Nice, France Springer. 2012; pp. 255-64.
[http://dx.doi.org/10.1007/978-3-642-33612-6_27]
[75]
Tzimiropoulos G, Argyriou V, Stathaki T. Subpixel registration with gradient correlation. IEEE Trans Image Process 2011; 20(6): 1761-7.
[http://dx.doi.org/10.1109/TIP.2010.2095867] [PMID: 21118776]

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