Abstract
Accurate registration of chest radiographs plays an increasingly important role in medical applications. However, most current intensity-based registration methods rely on the assumption of intensity conservation that is not suitable for alignment of chest radiographs. In this study, we propose a novel algorithm to match chest radiographs, for which the conventional residual complexity (RC) is modified as the similarity measure and the cubic B-spline transformation is adopted for displacement estimation. The modified similarity measure is allowed to incorporate the neighborhood influence into variation of intensity in a justified manner of the weight, while the transformation is implemented with a registration framework of pyramid structure. The results show that the proposed algorithm is more accurate in registration of chest radiographs, compared with some widely used methods such as the sum-of-squared-differences (SSD), correlation coefficient (CC) and mutual information (MI) algorithms, as well as the conventional RC approaches.
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ISHIDA T, KATSURAGAWA S, NAKAMURA K, et al. Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change [J]. Medical Physics, 1999, 26(7): 1320–1329.
LI M, CASTILLO E, LUO H Y, et al. Deformable image registration for temporal subtraction of chest radiographs [J]. International Journal of Computer Assisted Radiology & Surgery, 2014, 9(4): 513–522.
HILL D L G, BATCHELOR P G, HOLDEN M, et al. Medical image registration [J]. Physics in Medicine and Biology, 2001, 46(3): 1–45.
ZITOVA B, FLUSSER J. Image registration methods: A survey [J]. Image and Vision Computing, 2003, 21(11): 977–1000.
HARRIS C G, STEPHENS M. A combined corner and edge detector [C]// Proceedings of the 4th Alvey Vision Conference. Alvey, UK: [s.n.], 1988: 147–151.
SMITH S M, BRADY J M. SUSAN: A new approach to low level image processing [J]. International Journal of Computer Vision, 1997, 23(1): 45–78.
MORADI M, ABOLMAESUMI P. Medical image registration based on distinctive image features from scale-invariant (SIFT) key-points [J]. International Congress Series, 2005, 1281: 1292.
SANG Q, ZHANG J Z, YU Z Y. Robust non-rigid point registration based on feature-dependant finite mixture model [J]. Pattern Recognition Letters, 2013, 34(13): 1557–1565.
CRUM W R, HARTKENS T, HILL D L G. Nonrigid image registration: Theory and practice [J]. The British Journal of Radiology, 2004, 77(Sup 2): 140–153.
BROIT C. Optimal registration of deformed images [J]. Acta Obstetrica Et Gynaecologica Japonica, 1981, 39(4): 556–562.
CHRISTENSEN G E, RABBITT R D, MILLER M I. Deformable templates using large deformation kinematics [J]. IEEE Transactions on Image Processing, 1996, 5(10): 1435–1447.
THIRION J P. Image matching as a diffusion process: An analogy with Maxwell’s demons [J]. Medical Image Analysis, 1998, 2(3): 243–260.
YANG J, WANG Y T, TANG S Y, et al. Multiresolution elastic registration of X-ray angiography images using thin-plate spline [J]. IEEE Transactions on Nuclear Science, 2007, 54(1): 152–166.
MYRONENKO A, SONG X B. Intensity-based image registration by minimizing residual complexity [J]. IEEE Transactions on Medical Imaging, 2010, 29(11): 1882–1891.
NOCEDAL J, WRIGHT S J. Numerical optimization [M]. Berlin, Germany: Springer Science & Business Media, 2006.
KLEIN S, STARING M, PLUIM J P W. Evaluation of optimization methods for nonrigid medical image registration using mutual information and B-splines [J]. IEEE Transactions on Image Processing, 2007, 16(12): 2879–2890.
PENNEY G P, WEESE J, LITTLE J A, et al. A comparison of similarity measures for use in 2D-3D medical image registration [C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 1998: 1153–1161.
STRANG G. The discrete cosine transform [J]. SIAM Review, 1999, 41(1): 135–147.
BROWN L G. A survey of image registration techniques [J]. ACM Computing Surveys, 1992, 24(4): 325–376.
MAES F, COLLIGNON A, VANDERMEULEN D, et al. Multimodality image registration by maximization of mutual information [J]. IEEE Transactions on Medical Imaging, 1997, 16(2): 187–198.
CHEN Z, HAYKIN S. On different facets of regularization theory [J]. Neural Computation, 2002, 14(12): 2791–2846.
RUECKERT D, SONODA L I, HAYES C, et al. Nonrigid registration using free-form deformations: Application to breast MR images [J]. IEEE Transactions on Medical Imaging, 1999, 18(8): 712–721.
HOLDEN M. A review of geometric transformations for nonrigid body registration [J]. IEEE Transactions on Medical Imaging, 2008, 27(1): 111–128.
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Foundation item: the Fundamental Research Funds for the Central Universities of China (No. 30918011104), the National Natural Science Foundation of China (Nos. 61501241 and 61571230), the Natural Science Foundation of Jiangsu Province (No. BK20150792), the Foundation of Shandong Provincial Key Laboratory of Digital Medicine and Computer assisted Surgery (No. SDKL-DMCAS-2018-04), the China Postdoctoral Science Foundation (No. 2015M570450), and the Visiting Scholar Foundation of Key Laboratory of Biorheological Science and Technology (Chongqing University) of Ministry of Education (No. CQKLBST-2018-011)
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Xiang, Z., Li, M., Xiao, L. et al. Deformable Registration of Chest Radiographs Using B-spline Based Method with Modified Residual Complexity. J. Shanghai Jiaotong Univ. (Sci.) 24, 226–232 (2019). https://doi.org/10.1007/s12204-019-2056-8
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DOI: https://doi.org/10.1007/s12204-019-2056-8