Skip to main content
Log in

Automatic Medical Image Registration Based on an Integrated Method Combining Feature and Area Information

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Multi-modal image registration plays an increasing role in diagnosis, surveillance, and treatment of disease. This paper proposes a new registration measure, called contour and neighbor volume similarity method, which incorporates the merits of both area-based and feature-based methods. The implementation of this integrated method can be illustrated with a coarse-to-fine registration framework. In the coarse registration stage, the closed contours of the objects are first extracted as a stable feature set. Based on a distance measure, the feature set is used to rapidly estimate an initial transformation in the global scope. Subsequently, in response to the possible false alignment when registering symmetrical objects with feature-based methods, we employ an alignment correction procedure to ensure the reliability of the original transformation. Finally, the modified feature neighborhood and mutual information, an area-based method characterized by multiscale filtering mechanism, is adopted in the fine registration stage to obtain a precise final transformation. In addition, we introduce a differential evolution algorithm with an equilibrium strategy for estimating transformation parameters in the coarse registration stage. Our proposed method has been extensively evaluated by comparing with several state-of-the-art registration approaches on multi-modal brain images. The results indicate that it can automatically align images in various environments (different shapes of targets or different noise levels) with high accuracy and robustness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Sotiras A, Davatzikos C, Paragios N (2013) Deformable medical image registration: a survey. IEEE Trans Med Imaging 32(7):1153–1190

    Article  Google Scholar 

  2. Saha P, Strand R, Borgefors G (2015) Digital topology and geometry in medical imaging: a survey. IEEE Trans Med Imaging 34(9):1940–1964

    Article  Google Scholar 

  3. Zhou Z, Yang C, Chen B et al (2016) Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Tran Inf Syst 99(6):1531–1540

    Article  Google Scholar 

  4. Termenon M, Graña M (2013) Extreme learning machines for feature selection and classification of cocaine dependent patients on structural MRI data. Neural Process Lett 38:375–387

    Article  Google Scholar 

  5. Lu Y, Sun Y, Liao R et al (2014) A pre-operative CT and non-contrast-enhanced C-arm CT registration framework for trans-catheter aortic valve implantation. Comput Med Imaging Graph 38(8):683–695

    Article  Google Scholar 

  6. Cifor A, Risser L, Chung D et al (2013) Hybrid feature-based diffeomorphic registration for tumor tracking in 2-D liver ultrasound images. IEEE Trans Med Imaging 32(9):1647–1656

    Article  Google Scholar 

  7. Ghassabi Z, Shanbehzadeh J, Mohammadzadeh A (2016) A structure-based region detector for high-resolution retinal fundus image registration. Biomed Signal Process Control 23:52–61

    Article  Google Scholar 

  8. Maintz JBA, Elsen PAVD, Viergever MA (1996) Comparison of edge-based and ridge-based registration of CT and MR brain images. Med Image Anal 1(2):151–161

    Article  Google Scholar 

  9. Tavakoli V, Amini AA (2013) A survey of shaped-based registration and segmentation techniques for cardiac images. Comput Vision Image Underst 117(9):966–989

    Article  Google Scholar 

  10. Maris BM, Fiorini P (2015) Generalized shapes and point sets correspondence and registration. J Math Imaging Vis 52(2):218–233

    Article  MathSciNet  MATH  Google Scholar 

  11. Zhou Z, Wang Y, Wu Q et al (2017) Effective and Efficient Global Context Verification for Image Copy Detection. IEEE Trans Inf Forensics Secur 12(1):48–63

    Article  Google Scholar 

  12. Urschler M, Bauer J, Ditt H et al (2006) SIFT and shape context for feature-based nonlinear registration of thoracic CT images. Comput Vis Approaches Med Image Anal 4241:73–84

    Article  Google Scholar 

  13. Cideciyan AV (1995) Registration of ocular fundus images. IEEE Eng Med Biol Mag 14:52–58

    Article  Google Scholar 

  14. Guan Y, Sun Z (1999) Symmetric phase-matched filtering algorithms based on fourier-mellin transform. J Infrared Millimeter Waves 18(6):465–471

    Google Scholar 

  15. Maes F, Collignon A, Vandermeulen D et al (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2):187–198

    Article  Google Scholar 

  16. Viola P, Wells WM (1997) Alignment by maximization of mutual information. Int J Comput Vis 24:137–154

    Article  Google Scholar 

  17. Shu L, Chung ACS (2010) Feature based nonrigid brain MR image registration with symmetric alpha stable filters. IEEE Trans Med Imaging 29(1):106–119

    Article  Google Scholar 

  18. Lee H, Lee J, Kim N et al (2008) Robust feature-based registration using a Gaussian-weighted distance map and brain feature points for brain PET/CT images. Comput Biol Med 38(9):945–961

    Article  Google Scholar 

  19. Pluim JPW, Maintz JBA, Viergever MA (2000) Image registration by maximization of combined mutual information and gradient information. IEEE Trans Med Imaging 19(8):809–814

    Article  Google Scholar 

  20. Mohanalin, Beenamol, Kalra PK et al (2010) An automatic image registration scheme using Tsallis entropy. Biomed Signal Process Control 5(4):328–335

    Article  Google Scholar 

  21. Legg PA, Rosin PL, Marshall D et al (2015) Feature neighbourhood mutual information for multi-modal image registration: an application to eye fundus imaging. Pattern Recogn 48(6):1937–1946

    Article  Google Scholar 

  22. Legg PA, Rosin PL, Marshall D, Morgan JE (2009) A robust solution to multi-modal image registration by combining mutual information with multi-scale derivatives. In: Proceedings of 12th international conference on medical image computing and computer-assisted intervention (MICCAI), vol 5761, pp 616–623

  23. Lu G, Yan J, Kou Y et al (2011) Image registration based on criteria of feature point pair mutual information. IET Image Process 5(6):560–566

    Article  MathSciNet  Google Scholar 

  24. Rubeaux M, Nunes JC, Albera L et al (2014) Medical image registration using edgeworth-based approximation of mutual information. IRBM 35(3):139–148

    Article  Google Scholar 

  25. Liang J, Liu X et al (2014) Automatic registration of multisensor images using an integrated spatial and mutual information (SMI) metric. IEEE Trans Geosci Remote Sens 52(1):603–615

    Article  MathSciNet  Google Scholar 

  26. Kovesi P (2000) Phase congruency: a low-level image invariant. Psychol Res 64(2):136–148

    Article  Google Scholar 

  27. Dubuisson MP, Jain AK (1994) A modified Hausdorff distance for object matching. In: Proceedings of 12th IAPR international conference on image processing, pp 566–568

  28. Maes F, Collignon A, Vandermeulen D et al (1996) Multi-modality image registration by maximization of mutual information. In: Proceedings of workshop on mathematical methods in biomedical image analysis (MMBIA), pp 14–22

  29. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  30. Kovesi P (2016) Edge linking and line segment fitting. http://www.peterkovesi.com/matlabfns/index.html. Accessed 20 Sept 2016

  31. Li C, Yu X, Huang T et al (2016) A generalized hopfield network for nonsmooth constrained convex optimization: lie derivative approach. IEEE Trans Neural Netw Learn Syst 27(2):308–321

    Article  MathSciNet  Google Scholar 

  32. Li C, Yu X, Huang T et al (2017) Distributed optimal consensus over resource allocation network and its application to dynamical economic dispatch. IEEE Trans Neural Netw Learn Syst 99:1–12

    Google Scholar 

  33. Rueckert D, Sonoda LI, Hayes C et al (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18(8):712–721

    Article  Google Scholar 

  34. Huang X, Paragios N, Metaxas DN (2006) Shape registration in implicit spaces using information theory and free form deformations. IEEE Trans Pattern Anal Mach Intell 28(8):1303–1318

    Article  Google Scholar 

  35. Hager WW, Zhang H (2006) A survey of nonlinear conjugate gradient methods. Pac J Optim 2(1):35–58

    MathSciNet  MATH  Google Scholar 

  36. Postelnicu G, Zollei L, Fischl B (2009) Combined volumetric and surface registration. IEEE Trans Med Imaging 28(4):508–522

    Article  Google Scholar 

  37. Pickering MR, Muhit AA, Scarvell JM et al (2009) A new multi-modal similarity measure for fast gradient-based 2d-3d image registration. In: IEEE annual international conference of engineering in medicine and biology society (EMBC), pp 5821–5824

  38. Ashburner J, Friston KJ (2011) Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage 55(3):954–967

    Article  Google Scholar 

  39. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  40. McConnell Brain Imaging Center Montreal Neurological Institute, McGill University (2016) BrainWeb. http://mouldy.bic.mni.mcgill.ca/brainweb. Accessed 20 Sept 2016

  41. National Institute of Biomedical Imaging and Bioengineering, Vanderbilt University (2016) The Retrospective Image Registration Project. http://www.insight-journal.org/rire/. Accessed by 20 Sept 2016

  42. Reel PS, Dooley LS, Wong KCP et al (2013) Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure. In: IEEE international conference on image processing (ICIP), pp 1428–1432

  43. Gong W, Cai Z et al (2011) Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans Syst Man Cybern Part B Cybern 41(2):397–413

    Article  Google Scholar 

  44. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: IEEE world congress on computational intelligence, pp 69–73

  45. Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Euro J Oper Res 185(3):1155–1173

    Article  MathSciNet  MATH  Google Scholar 

  46. Ursem R (2002) Diversity-guided evolutionary algorithms. In: Proceedings of parallel problem solving from nature (PPSN), pp 462–471

  47. Myronenko A, Song X (2010) Point Set Registration: coherent Point Drift. IEEE Trans Pattern Anal Mach Intell 32(12):2262–2275

    Article  Google Scholar 

  48. Cunningham SC, Adhami RR (2014) Mammogram iterative pompeiu-hausdorff registration algorithm. In: IEEE international conference on image processing (ICIP), pp 3557–3561

  49. Li Q, Ji H (2013) Multimodality image registration using local linear embedding and hybrid entropy. Neurocomputing 111:34–42

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Nature Science Foundation of China (No. 61571236) and the Research Committee of University of Macau (MYRG2015-00011-FST, MYRG2015-00012-FST), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No.16KJB520032), and Postgraduate Research & Practice Innovation Program of Jiangsu Province.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Baoyun Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, J., Pun, CM., Pan, Z. et al. Automatic Medical Image Registration Based on an Integrated Method Combining Feature and Area Information. Neural Process Lett 49, 263–284 (2019). https://doi.org/10.1007/s11063-018-9808-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9808-6

Keywords

Navigation