Commenced in January 2007
Paper Count: 31108
A Review on Medical Image Registration Techniques
Abstract:This paper discusses the current trends in medical image registration techniques and addresses the need to provide a solid theoretical foundation for research endeavours. Methodological analysis and synthesis of quality literature was done, providing a platform for developing a good foundation for research study in this field which is crucial in understanding the existing levels of knowledge. Research on medical image registration techniques assists clinical and medical practitioners in diagnosis of tumours and lesion in anatomical organs, thereby enhancing fast and accurate curative treatment of patients. Literature review aims to provide a solid theoretical foundation for research endeavours in image registration techniques. Developing a solid foundation for a research study is possible through a methodological analysis and synthesis of existing contributions. Out of these considerations, the aim of this paper is to enhance the scientific community’s understanding of the current status of research in medical image registration techniques and also communicate to them, the contribution of this research in the field of image processing. The gaps identified in current techniques can be closed by use of artificial neural networks that form learning systems designed to minimise error function. The paper also suggests several areas of future research in the image registration.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316614Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1063
 Markelj P, Tomaˇzeviˇc D, Likar B and Pernuˇs F. A review of 3D/2D registration methods for image-guided interventions. Medical image analysis. 2012; 16(3):642–661.
 Fluck O, Vetter C, Wein W, Kamen A, Preim B and Westermann R. A survey of medical image registration on graphics hardware. Computer methods and programs in biomedicine. 2011; 104(3):e45–e57.
 Viergever Max A, Maintz JB, Antoine, Klein, Stefan, Murphy, Keelin, Staring, Marius, Pluim and Josien PW. A survey of medical image registration–under review. Medical Image Analysis. Elsevier; 2016; 33:140-144 .
 Oliveira FPM and Tavares JMRS. Medical image registration: a review. Computer methods in biomechanics and biomedical engineering, Taylor & Francis. 2014; 17(2):73–93.
 Sarrut D, Baudier T, Ayadi M, Tanguy R and Rit S. Deformable image registration applied to lung SBRT: Usefulness and limitations. Physica Medica: European Journal of Medical Physics, Elsevier; 2017; 44: p. 108–112.
 Zhou W and Xie Y. Interactive Multigrid refinement for deformable image registration. BioMed research international, Hindawi Publishing Corporation. 2013.
 Gupta A, Verma HK and Gupta S. Technology and research developments in carotid image registration. Biomedical Signal Processing and Control. 2012; 7(6):560–570.
 Sotiras Aristeidis, Davatzikos Christos and Paragios Nikos. Deformable medical image registration: A survey. IEEE transactions on medical imaging. IEEE; 2013; 32(7):1153–1190.
 Ezzeldeen RM, Ramadan HH, Nazmy TM, Yehia MA and Abdel WMS. Comparative study for image registration techniques of remote sensing images. The Egyptian Journal of Remote Sensing and Space Science. Elsevier; 2010; 13(1):31–36.
 Cavoretto R, De Rossi A, Freda R, Qiao H and Venturino E. Numerical Methods for Pulmonary Image Registration. arXiv preprint arXiv:1705.06147, 2017.
 Van der Bom I, Klein S, Staring M, Homan R, Bartels L and Pluim J. Evaluation of optimization methods for intensity-based 2D-3D registration in x-ray guided interventions. In: SPIE Medical Imaging. International Society for Optics and Photonics; 2011. p. 796223–796223.
 Song H and Qiu P. Intensity-based 3D local image registration. Pattern Recognition Letters. Elsevier; 2017; 94:15–21.
 Wu G, Kim M, Wang Q, Gao Y, Liao S and Shen D. Unsupervised deep feature learning for deformable registration of MR brain images. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. 2013; p. 649–656.
 Toth D, Panayiotou M, Brost A, Behar JM, Rinaldi CA, Rhode KS and Mountney P. 3D/2D Registration with superabundant vessel reconstruction for cardiac resynchronization therapy. Medical image analysis, Elsevier. 2017; 42:160–172.
 Wang J, Brown MS and Tan CL. Automatic corresponding control points selection for historical document image registration. 10th International Conference on Document Analysis and Recognition, IEEE. 2009; p. 1176–1180.
 Dung LR, Huang CM and Wu YY. Implementation of RANSAC algorithm for feature-based image registration. Journal of Computer and Communications, Scientific Research Publishing; 2013. 1(06):46, 2013.
 Hossein-nejad Z and Nasri M. Image registration based on SIFT features and adaptive RANSAC transform. Communication and Signal Processing (ICCSP), 2016 International Conference on; IEEE; 2014; 1087–1091.
 Zrour R, Kenmochi Y, Talbot H, Buzer L, Hamam Y, Shimizu I and Sugimoto A. Optimal consensus set for digital line and plane fitting. International Journal of Imaging Systems and Technology, Wiley Online Library; 21(1):45–57, 2011.
 Hopp T, Dietzel M, Baltzer PA, Kreisel P, Kaiser WA and Gemmeke H, et al. Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization. Medical image analysis. 2013; 17(2):209–218.
 Klein S, Staring M, Murphy K, Viergever MA and Pluim JP. Elastix: a toolbox for intensity-based medical image registration. Medical Imaging, IEEE Transactions on. 2010; 29(1):196–205.
 Lu X, Ma H and Zhang B. A non-rigid medical image registration method based on improved linear elastic model. Optik-International Journal for Light and Electron Optics. 2012; 123(20):1867–1873.
 Bunting P, Labrosse F and Lucas R. A multi-resolution area-based technique for automatic multi-modal image registration. Image and Vision Computing. 2010; 28(8):1203–1219.
 Kosi´nski W, Michalak P and Gut P. Robust Image Registration Based on Mutual Information Measure. Journal of Signal and Information Processing. 2012; 3:175.
 Lin L, Jin C, Fu Z, Zhang B, Bin G and Wu S. Predicting healthy older adult’s brain age based on structural connectivity networks using artificial neural networks. Computer methods and programs in biomedicine. 2016; 125:8–17.
 Guo D, Qiu T, Bian J, Kang W and Zhang L. A computer-aided diagnostic system to discriminate SPIO-enhanced magnetic resonance hepatocellular carcinoma by a neural network classifier. Computerized Medical Imaging and Graphics. 2009; 33(8):588–592.
 Schreibmann E, Thorndyke B, Li T, Wang J and Xing L. Four-dimensional image registration for image-guided radiotherapy. In: International Journal of Radiation Oncology, Biology and Physics. Elsevier; 2008; 71(2): p. 578–586.
 Mezura-Montes E, Acosta-Mesa HG, Ram´ırez-Garc´es DS, Cruz-Ram´ırez N and Hern´andez-Jim´enez R. An image registration method for colposcopic images. Computational and mathematical methods in medicine, Hindawi Publishing Corporation. 2013.
 Liu P, Eberhardt B, Wybranski C, Ricke J, and L¨udemann, L. Nonrigid 3D medical image registration and fusion based on deformable models. Computational and mathematical methods in medicine, Hindawi Publishing Corporation. 2013.
 Rueckert D and Aljabar P. Nonrigid registration of medical images: Theory, methods, and applications
[applications corner]. Signal Processing Magazine, IEEE. 2010; 27(4):113–119.
 Aktar N, Alam J and Pickering M. A non-rigid 3D multi-modal registration algorithm using partial volume interpolation and the sum of conditional variance. Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on, IEEE; p. 1–7, 2014.
 Zhang J, Chen L, Wang X, Teng Z, Brown AJ, Gillard JH, Guan Q and Chen S. Compounding local invariant features and global deformable geometry for medical image registration. PloS one. Public Library of Science, 2014; 9(8):e105815.
 Kahaki SMM, Nordin MJ, Ashtari AH, and Zahra SJ. Invariant feature matching for image registration application based on new dissimilarity of spatial features. PloS one. Public Library of Science, 2016; 11(3):e0149710.
 Lu Y, Gao K, Zhang T and Xu T. A novel image registration approach via combining local features and geometric invariants. PloS one. Public Library of Science, 2018; 13(1):e0190383.
 Liu C, Ma J, Ma Y and Huang J. Retinal image registration via feature-guided Gaussian mixture model. Journal of the Optical Society of America A, Optical Society of America; 2016. 33(7):1267–1276.
 Li Z, Huang F, Zhang J, Dashtbozorg B, Abbasi-Sureshjani S, Sun Y, Long X, Yu Q, ter Haar Romeny B and Tan T. Multi-modal and multi-vendor retina image registration. Biomedical Optics Express, Optical Society of America. 2018; (9)2:410–422.
 Ravikumar N, Gooya A, C¸ imen S, Frangi AF and Taylor ZA. Group-wise similarity registration of point sets using Students t-mixture model for statistical shape models. In: Medical image analysis, Elsevier; 2018; 44: p. 156–176.
 Ma J, Jiang J, Chen J, Liu C and Li C. Multimodal retinal image registration using edge map and feature guided Gaussian mixture model. Visual Communications and Image Processing (VCIP). IEEE; 2016. p. 1–4.
 Li Z, Mahapatra D, Tielbeek JAW, Stoker J, van Vliet LJ and Vos FM. Image registration based on autocorrelation of local structure. IEEE transactions on medical imaging, IEEE. 2016; 35(1):63–75.
 Zhong Z, Guo X, Cai Y, Yang Y, Wang J, Jia X and Mao W. 3D-2D Deformable Image Registration Using Feature-Based Nonuniform Meshes. BioMed Research International, Hindawi; 2016.
 Esther Dura, Juan Domingo, Guillermo Ayala and Luis Mart´ı-Bonmat´ı. Evaluation of the registration of temporal series of contrast-enhanced perfusion magnetic resonance 3D images of the liver. Computer methods and programs in biomedicine. Elsevier. 2012; 1083:932–945.
 Manjusha Deshmukh and Udhav Bhosle. A survey of image registration. International Journal of Image Processing (IJIP), 5(3):245–269, 2011.
 Crum WR, Hartkens T and Hill D. Non-rigid image registration: theory and practice. The British Journal of Radiology. 2014.
 Delibasis KK, Asvestas PA and Matsopoulos GK. Automatic point correspondence using an artificial immune system optimization technique for medical image registration. computerized medical imaging and graphics. 2011; 35(1):31–41.
 Meskine F, Taleb N, El-Mezouar MC, Kpalma K and Almhdie A, et al. A rigid point set registration of remote sensing images based on genetic algorithms & Hausdorff distance. World Academy of Science, Engineering and Technology. 2013; p. 1095–1100.
 Risser L, Vialard FX, Murgasova M, Holm D and Rueckert D. Large deformation diffeomorphic registration using fine and coarse strategies. In: Biomedical Image Registration. Springer; 2010. p. 186–197.
 Arguill`ere S, Miller M and Younes L. LDDMM Surface Registration with Atrophy Constraints. arXiv preprint arXiv:150300765. 2015.
 Ceritoglu C, Wang L, Selemon LD, Csernansky JG, Miller MI and Ratnanather JT. Large deformation diffeomorphic metric mapping registration of reconstructed 3D histological section images and in vivo MR images. Frontiers in human neuroscience. 2010; 4.
 Pai A, Sommer S, Darkner S, Sørensen L, Sporring J and Nielsen M. Stepwise inverse consistent Eulers scheme for diffeomorphic image registration. In: Biomedical Image Registration. Springer; 2014. p. 223–230.
 Lombaert H, Grady L, Pennec X, Peyrat JM, Ayache N and Cheriet F. Groupwise spectral Log-Demons framework for atlas construction. In: Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. Springer; 2013. p. 11–19.
 Goshtasby AA. Image registration: Principles, tools and methods. Springer Science & Business Media 2012.
 Qiu Z, Tang H and Tian D. Non-rigid medical image registration based on the thin-plate spline algorithm. Computer Science and Information Engineering, 2009 WRI World Congress on, IEEE. 2009; 2. 522–527.
 Menon HP and Narayanankutty KA. Applicability of non-rigid medical image registration using moving least squares. International Journal of Computer Applications. 2010; 1(6): p. 79–86.
 Han X. Feature-constrained nonlinear registration of lung CT images. Medical image analysis for the clinic: a grand challenge. 2010; p. 63–72.
 Klein S, Staring M and Pluim JPW. Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines. IEEE transactions on image processing. 2007; 16(12):2879–2890.
 Yaegashi Yuji, Tateoka Kunihiko, Fujimoto Kazunori, Nakazawa Takuya, Nakata Akihiro, Saito Yuichi, Abe Tadanori, Yano Masaki and Sakata K. Assessment of similarity measures for accurate deformable image registration. Journal of Nuclear medicine and Radiation Therapy. Elsevier; 2012; 3(4).
 Glocker BM. Random fields for image registration. Technical University Munich, 2011.
 Bernd Fischer and Jan Modersitzki. Ill-posed medicine-an introduction to image registration. Inverse Problems, 24(3):034008, 2008.
 Dr´eo J, Nunes JC and Siarry P. Robust rigid registration of retinal angiograms through optimization. Computerized Medical Imaging and Graphics, Elsevier. 2006; 30(8):453–463.
 Dasgupta B, Divya K, Mehta VK and Deb K. RePAMO: Recursive Perturbation Approach for Multimodal Optimization. Engineering Optimization. 2013; 45(9):1073–1090.
 Chen C. Stochastic simulation optimization: an optimal computing budget allocation. vol. 1. World scientific. 2010.
 de Groot M, Vernooij MW, Klein S, Ikram MA, Vos FM and Smith SM, et al. Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration. NeuroImage. 2013; 76:400–411.
 Micha¨el Baudin, Vincent Couvert and Serge Steer. Optimization in scilab. Technical report, Technical report, Scilab Consortium, July 2010. http://forge. scilab. org/index. php/p/docoptimscilab, 2010.
 Klein S, Staring M, Andersson P and Pluim JP. Preconditioned stochastic gradient descent optimisation for monomodal image registration. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011. Springer; 2011. p. 549–556.
 Ralf Floca and Hartmut Dickhaus. A flexible registration and evaluation engine (free). Computer methods and programs in biomedicine, 87(2):81–92, 2007.
 Stefan Klein. Optimisation methods for medical image registration. PhD thesis, Image Sciences Institute, UMC Utrecht, 2008.
 Stefan Klein, Josien PW Pluim, Marius Staring and Max A Viergever. Adaptive stochastic gradient descent optimisation for image registration. International journal of computer vision, 81(3):227–239, 2009.
 Qiao Y, van Lew B, Lelieveldt BPF and Staring M. Fast automatic step size estimation for gradient descent optimization of image registration. IEEE transactions on medical imaging, IEEE; 35(2):391–403. 2016.
 Guo Y, Li J, Zhang P, Shao Q, Xu M and Li Y. Comparative evaluation of target volumes defined by deformable and rigid registration of diagnostic PET/CT to planning CT in primary esophageal cancer. Medicine, Wolters Kluwer Health. 2017; 96(1): p e5528.
 Rigaud B, Simon A, Castelli J, Gobeli M, Ospina AJD, Cazoulat G, Henry O, Haigron P and De Crevoisier R. Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy. BioMed Research International, Hindawi; 2015.
 Wodzinski M, Skalski A, Ciepiela I, Kuszewski T, Kedzierawski P and Gajda J. Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms. Physics in medicine and biology, IOP Publishing; 63(3). 2018.
 Setio AAA and others. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Medical image analysis, Elsevier; 42:1–13, 2017.
 Tang M and Chen F. A qualitative meta analysis review on medical image registration evaluation. Procedia Engineering. Elsevier; 2012; 29:499–503 .
 Ou Y, Akbari H, Bilello M, Da X and Davatzikos C. Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights. IEEE transactions on medical imaging. IEEE; 2014; 33(10):2039–2065.
 Werner R, Schmidt-Richberg A, Handels H and Ehrhardt J. Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study. Physics in medicine and biology, IOP Publishing; 59(15): 4247. 2014.
 Razlighi QR, Kehtarnavaz N and Yousefi S. Evaluating similarity measures for brain image registration. Journal of visual communication and image representation, Elsevier; 24(7):977–987, 2013.
 Mahmoudzadeh AP and Kashou NH. Evaluation of interpolation effects on upsampling and accuracy of cost functions-based optimized automatic image registration. Journal of Biomedical Imaging, Hindawi Publishing Corp.; 2013. p. 16. 2013.
 Kadoya N, Fujita Y, Katsuta Y and others. Evaluation of various deformable image registration algorithms for thoracic images. Journal of radiation research. Oxford University Press; 2014; 55(1):175–182.
 Shah SAA, Bennamoun M and Boussaid F. Performance evaluation of 3d local surface descriptors for low and high resolution range image registration. Digital lmage Computing: Techniques and Applications (DlCTA), 2014 International Conference on, IEEE; p. 1–7, 2014.