References
[1]. Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage, 54(3), 2033-2044. https://doi.org/10.1016/j. neuroimage.2010.09.025
[2]. Bajcsy, R., & Kovačič, S. (1989). Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing, 46(1), 1-21. https://doi.org/10.1016/S0734- 189X(89)80014-3
[3]. Bookstein, F. L. (1989). Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6), 567-585. https://doi.org/10.1109/34. 24792
[4]. Broit, C. (1981). Optimal registration of deformed images. University of Pennsylvania Philadelphia.
[5]. Bro-Nielsen, M., & Gramkow, C. (1996, September). Fast fluid registration of medical images. In International Conference on Visualization in Biomedical Computing (pp. 265-276). Springer, Berlin, Heidelberg. https://doi.org/ 10.1007/BFb0046964
[6]. Brown, L. G. (1992). A survey of image registration techniques. ACM Computing Surveys (CSUR), 24(4), 325- 376. https://doi.org/10.1145/146370.146374
[7]. Cao, X., Yang, J., Gao, Y., Guo, Y., Wu, G., & Shen, D. (2017a). Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis. Medical Image Analysis, 41, 18-31. https://doi.org/10. 1016/j.media.2017.05.004
[8]. Cao, X., Yang, J., Zhang, J., Nie, D., Kim, M., Wang, Q., & Shen, D. (2017b, September). Deformable image registration based on similarity-steered CNN regression. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 300-308). Springer, Cham. https://doi.org/10.1007/978-3-319- 66182-7_35
[9]. Cifor, A., Risser, L., Chung, D., Anderson, E. M., & Schnabel, J. A. (2012, May). Hybrid feature-based logdemons registration for tumour tracking in 2-D liver ultrasound images. In 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 724-727). IEEE. https://doi.org/10.1109/ISBI.2012.6235650
[10]. Guo, F., Zhao, X., Zou, B., & Liang, Y. (2017). Automatic retinal image registration using blood vessel segmentation and SIFT feature. International Journal of Pattern Recognition and Artificial Intelligence, 31(11), 1757006. https://doi.org/10.1142/S0218001417570063
[11]. Han, L., Hipwell, J. H., Tanner, C., Taylor, Z., Mertzanidou, T., Cardoso, J., ... & Hawkes, D. J. (2012). Development of patient-specific biomechanical models for predicting large breast deformation. Physics in Medicine and Biology, 57(2), 455-472. https://doi.org/10. 1088/0031-9155/57/2/455
[12]. Heinrich, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, M., & Schnabel, J. A. (2012). MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical Image Analysis, 16(7), 1423-1435. https://doi.org/10.1016/j. media.2012.05.008
[13]. Heinrich, M. P., Jenkinson, M., Brady, J. M., & Schnabel, J. A. (2011, July). Non-rigid image registration through efficient discrete optimization. In MIUA (pp. 187- 192).
[14]. Kim, M., Han, D. K., & Ko, H. (2016). Joint patch clustering-based dictionary learning for multimodal image fusion. Information Fusion, 27, 198-214. https://doi.org/10.1016/j.inffus.2015.03.003
[15]. Kim, M., Wu, G., Yap, P. T., & Shen, D. (2012). A general fast registration framework by Learning Deformation–Appearance Correlation. IEEE Trans. Image Process, 21(4), 1823-1833. https://doi.org/10.1109/ TIP.2011.2170698
[16]. Li, H., Qiu, H., Yu, Z., & Li, B. (2017). Multifocus image fusion via fixed window technique of multiscale images and non-local means filtering. Signal Processing, 138, 71- 85. https://doi.org/10.1016/j.sigpro.2017.03.008
[17]. Li, H., Yu, Z., & Mao, C. (2016). Fractional differential and variational method for image fusion and superresolution. Neurocomputing, 171, 138-148. https://doi. org/10.1016/j.neucom.2015.06.035
[18]. Li, S., Kang, X., Fang, L., Hu, J., & Yin, H. (2017). Pixellevel image fusion: A survey of the state of the art. Information Fusion, 33, 100-112. https://doi.org/10.1016/ j.inffus.2016.05.004
[19]. Li, Y., & Verma, R. (2011). Multichannel image registration by feature-based information fusion. IEEE Transactions on Medical Imaging, 30(3), 707-720. https://doi.org/10.1109/tmi.2010.2093908
[20]. Liao, S., & Chung, A. C. (2011). Nonrigid brain MR image registration using uniform spherical region descriptor. IEEE Transactions on Image Processing, 21(1), 157-169. https://doi.org/10.1109/TIP.2011.2159615
[21]. Liu, C., Yuen, J., & Torralba, A. (2011). SIFT flow: Dense correspondence across scenes and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 978-994. https://doi.org/10. 1109/ tpami.2010.147
[22]. Mansi, T., Pennec, X., Sermesant, M., Delingette, H., & Ayache, N. (2011). iLogDemons: A demons-based registration algorithm for tracking incompressible elastic biological tissues. International Journal of Computer Vision, 92(1), 92-111. https://doi.org/10.1007/s11263- 010-0405-z
[23]. Pennec, X., Stefanescu, R., Arsigny, V., Fillard, P., & Ayache, N. (2005, October). Riemannian elasticity: A statistical regularization framework for non-linear registration. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 943-950). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_116
[24]. Rabbitt, R. D., Weiss, J. A., Christensen, G. E., & Miller, M. I. (1995, August). Mapping of hyperelastic deformable templates using the finite element method. In Vision Geometry IV (Vol. 2573, pp. 252-265). International Society for Optics and Photonics. https://doi.org/10.1 117/12.216419
[25]. Rivaz, H., Karimaghaloo, Z., Fonov, V. S., & Collins, D. L. (2014). Nonrigid registration of ultrasound and MRI using contextual conditioned mutual information. IEEE Transactions on Medical Imaging, 33(3), 708-725. https://doi.org/10.1109/tmi.2013.2294630
[26]. Roy, S., Wang, W. T., Carass, A., Prince, J. L., Butman, J. A., & Pham, D. L. (2014). PET attenuation correction using synthetic CT from ultrashort echo-time MR imaging. Journal of Nuclear Medicine, 55(12), 2071-2077.
[27]. So, R. W., Tang, T. W., & Chung, A. C. (2011). Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recognition, 44(10-11), 2450- 2467. https://doi.org/10.1016/j.patcog.2011.04.008
[28]. Toews, M., & Wells III, W. M. (2013). Efficient and robust model-to-image alignment using 3D scaleinvariant features. Medical Image Analysis, 17(3), 271- 282. https://doi.org/10.1016/j.media.2012.11.002
[29]. Tu, Z., & Bai, X. (2010). Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(10), 1744-1757. https://doi.org/ 10.1109/tpami.2009.186
[30]. Vercauteren, T., Pennec, X., Perchant, A., & Ayache, N. (2009). Diffeomorphic demons: Efficient nonparametric image registration. NeuroImage, 45(1), S61- S72. https://doi.org/10.1016/j.neuroimage.2008.10.040
[31]. Wachinger, C., & Navab, N. (2012). Entropy and Laplacian images: Structural representations for multimodal registration. Medical Image Analysis, 16(1), 1-17. https://doi.org/10.1016/j.media.2011.03.001
[32]. Wang, K., Qi, G., Zhu, Z., & Chai, Y. (2017). A novel geometric dictionary construction approach for sparse representation based image fusion. Entropy, 19(7), 306. https://doi.org/10.3390/e19070306
[33]. Wei, L., Cao, X., Wang, Z., Gao, Y., Hu, S., Wang, L., ... & Shen, D. (2017). Learning based deformable registration for infant MRI by integrating random forest with auto context model. Medical Physics, 44(12), 6289-6303. https://doi.org/10.1002/mp.12578
[34]. Woo, J., Stone, M., & Prince, J. L. (2015). Multimodal registration via mutual information incorporating geometric and spatial context. IEEE Transactions on Image Processing, 24(2), 757-759. https://doi.org/10. 1109/TIP.2014.2387019
[35]. Wu, G., Kim, M., Wang, Q., & Shen, D. (2013). SHAMMER: Hierarchical attribute guided, symmetric diffeomorphic registration for MR brain images. Human Brain Mapping, 35(3), 1044-1060. https://doi.org/10. 1002/hbm.22233
[36]. Wu, G., Qi, F., & Shen, D. (2006). Learning-based deformable registration of MR brain images. IEEE Transactions on Medical Imaging, 25(9), 1145-1157. https://doi.org/10.1109/TMI.2006.879320
[37]. Wu, G., Yap, P. T., Kim, M., & Shen, D. (2010). TPSHAMMER: Improving HAMMER registration algorithm by soft correspondence matching and thin-plate splines based deformation interpolation. NeuroImage, 49(3), 2225-2233. https://doi.org/10.1016/j.neuroimage.2009. 10.065
[38]. Zhang, J., Gao, Y., Park, S. H., Zong, X., Lin, W., & Shen, D. (2017). Structured learning for 3-D perivascular space segmentation using vascular features. IEEE Transactions on Biomedical Engineering, 64(12), 2803- 2812. https://doi.org/10.1109/TBME.2016.2638918
[39]. Zhu, Z., Qi, G., Chai, Y., & Li, P. (2017). A geometric dictionary learning based approach for fluorescence spectroscopy image fusion. Applied Sciences, 7(2), 161. https://doi.org/10.3390/app7020161
[40]. Zhuang, X., Arridge, S., Hawkes, D. J., & Ourselin, S. (2011). A nonrigid registration framework using spatially encoded mutual information and free-form deformations. IEEE Transactions on Medical Imaging, 30(10), 1819-1828. https://doi.org/10.1109/TMI.2011.21 50240
[41]. Zitova, B., & Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21(11), 977-1000. https://doi.org/10.1016/S0262-8856(03) 00137-9