一种稳健的非刚性医学影像配准方法

doi:10.3969/j.issn.1001-2400.2014.05.010

Abstract

Abstract:

Non-rigid registration of medical images has become a challenging task in medical image processing and applications. In this paper, we propose a local linear embedding (LLE) and improved L-BFGS (limited-memory Broyden Fletcher Goldfarb Shanno) optimization based registration method. With abundant spatial information and good stability in noisy environment, the ordinal features are computed on different orientations to represent spatial information in medical images. For high dimensional ordinal features, the LLE algorithm is used for dimensionality reduction and the inverse mapping of LLE is used to fuse complementary information together. Then a hybrid entropy based similarity measure which integrates image intensity with ordinal feature is chosen as the registration function. Finally an improved L-BFGS algorithm is used to search for the optimal registration parameters. We evaluate the effectiveness of the proposed approach by applying it to the simulated brain image data. Experimental results show that the proposed registration algorithm is less sensitive to noise in images. Compared with some traditional methods, the proposed algorithm is of higher precision and better robustness.

Key words: non-rigid registration, local linear embedding, ordinal feature

CLC Number:

TP391.4

LI Qi;JI Hongbing;ZANG Bo;LIU Jin. Non-rigid registration of medical images based on local linear embedding and improved L-BFGS optimization[J].J4, 2014, 41(5): 54-60.

References

［1］ Sotiras A, Daratzikos C, Paragios N. Deformable Medical Image Registration: a Survey［J］. IEEE Transactions on Medical Imaging, 2013, 32(7): 1153-1190.
［2］ Liao S, Chung A C S. Nonrigid Brain MR Image Registration Using Uniform Spherical Region Descriptor［J］. IEEE Transactions on Image Processing, 2012, 21(1): 157-169.
［3］ Wang Shanhu, You Hongjian, Fu Kun. BFSIFT: a Novel Method to Find Feature Matches for SAR Image Registration［J］. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 649-653.
［4］ Pluim J P W, Maintz J B A, Viergever M A. Mutual-information-based Registration of Medical Images: a Survey［J］. IEEE Transactions on Medical Imaging, 2003, 22(8): 986-1004.
［5］ So R W K, Tang T W H, Chung A C S. Non-rigid Image Registration of Brain Magnetic Resonance Images Using Graph-cuts［J］. Pattern Recognition, 2011, 44(10-11): 2450-2467.
［6］ Pluim J P W, Maintz J B A, Viergever M A. Image Registration by Maximization of Combined Mutual Information and Gradient Information［J］. IEEE Transactions on Medical Imaging, 2000, 19(8): 809-814.
［7］ Rueckert D, Clarkson M J, Hill D L G, et al. Non-rigid Registration Using Higher-order Mutual Information ［C］//Proceedings of SPIE—the International Society for Optical Engineering. Bellingham: SPIE, 2000: 438-447.
［8］ Chen B J, Li J L, Chen G. Study of Medical Image Registration Based on Second-Order Mutual Information. ［C］//Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering. Piscataway: IEEE, 2007: 956-959.
［9］李琦, 姬红兵, 同鸣. 一种多模态医学影像鲁棒配准方法［J］. 西安电子科技大学学报, 2011, 38(6): 30-36.
Li Qi, Ji Hongbing, Tong Ming. Robust Registration of Multimodality Medical Images Based on the Principal Ordinal Feature and Hybrid Entropy［J］. Journal of Xidian University, 2011, 38(6): 30-36.
［10］ Tan T N, Sun Z N. Ordinal Representations for Biometrics Recognition ［C］//Proceedings of 15th European Signal Processing Conference. Poland: EURASIP, 2007: 35-39.
［11］ Han Y F, Tan T N, Sun Z N, et al. Embedded Palmprint Recognition System on Mobile Devices. ［C］//Lecture Notes in Computer Science: 4642. Heidelberg: Springer Verlag, 2007: 1184-1193.
［12］ Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding［J］. Science, 2000, 290(5500): 2323-2326.
［13］ Zhang C S, Wang J, Zhao N Y, et al. Reconstruction and Analysis of Multi-pose Face Images Based on Nonlinear Dimensionality Reduction［J］. Pattern Recognition, 2004, 37(2): 325-336.
［14］ Rueckert D, Sonoda L I, Hayes C, et al. Non-rigid Registration Using Free-form Deformations: Application to Breast MR Images［J］. IEEE Transactions on Medical Imaging, 1999, 18(8): 712-721.
［15］ Diez Y, Oliver A, Llado X, et al. Revisiting Intensity-based Image Registration Applied to Mammography［J］. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(5): 716-725.
［16］ Morales J L, Nocedal J. Remark on “Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-scale Bound Constrained Optimization”［J］. ACM Transactions on Mathematical Software, 2011, 38(1): 7.

[1]	ZHENG Xiang;TONG Ming;CAO Yang. LBM craniocerebral MR 3D medical image non-rigid registration method [J]. J4, 2013, 40(6): 125-131.
[2]	TONG Ming;CAO Yang;HE Mengyue;ZHANG Jianlong. Non-rigid registration method of the medical image based on the LBM [J]. J4, 2013, 40(1): 6-13.
[3]	LI Qi;JI Hongbing;TONG Ming. Robust registration of multimodality medical images based on the principal ordinal feature and hybrid entropy [J]. J4, 2011, 38(6): 30-36+145.

Non-rigid registration of medical images based on local linear embedding and improved L-BFGS optimization

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Metrics

Comments

Recommended 0