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 Volume 5, Issue 2 (February 2018), Pages: 171-175


 Original Research Paper

 Title: Structural information in the shape of the optimum of registration objective function

 Author(s): Sri Purwani 1, *, Julita Nahar 1, Asep K. Supriatna 1, Carole Twining 2


 1Department of Mathematics, Padjadjaran University, Bandung, Indonesia
 2Department of Imaging Science, The University of Manchester, Manchester, United Kingdom

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Registration is a way to find meaningful correspondences between points in one image to points in another image or a group of images. It attempts to align images, such that common structures match. In conventional pairwise intensity-based registration, we usually attempt to find the optimum of registration objective function. We investigated whether there is structural information present in the shape of the optimum. Such structures might be used to improve the performance of registration. By using simple structures (i.e., an edge or corner structure) and Mutual Information (MI) objective function, we perturbed one image locally with a diffeomorphism, and found interesting structure in the shape of the quality of fit function. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Registration, Structural information, Diffeomorphism, Mutual information

 Article History: Received 25 February 2017, Received in revised form 23 November 2017, Accepted 15 December 2017

 Digital Object Identifier:


 Purwani S, Nahar J, Supriatna AK, and Twining C (2018). Structural information in the shape of the optimum of registration objective function. International Journal of Advanced and Applied Sciences, 5(2): 171-175

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 References (19)

  1. Ashburner J and Friston KJ (2007). Rigid body registration. In: Friston KJ (Ed.), Statistical parametric mapping: The analysis of fuctional brain images: 49-62. Academic Press, London, UK. 
  2. Collignon A, Maes F, Delaere D, Vandermeulen D, Suetens P, and Marchal G (1995). Automated multi-modality image registration based on information theory. Information Processing in Medical Imaging, 3(6): 263-274.     
  3. Cootes TF, Marsland S, Twining CJ, Smith K, and Taylor CJ (2004). Groupwise diffeomorphic non-rigid registration for automatic model building. In: Pajdla T and Matas J (Eds.), Computer vision - ECCV 2004. ECCV 2004. Lecture notes in computer science, 3024: 316-327. Springer, Berlin, Heidelberg. 
  4. Cootes TF, Twining CJ, Petrovic VS, Babalola KO, and Taylor CJ (2010). Computing accurate correspondences across groups of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11): 1994-2005.  PMid:20847389 
  5. Haber E and Modersitzki J (2006). Intensity gradient based registration and fusion of multi-modal images. In: Larsen R, Nielsen M, and Sporring J (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2006. MICCAI 2006. Lecture notes in computer science, 4191: 726-733. Springer, Berlin, Heidelberg. 
  6. Konukoglu E, Criminisi A, Pathak S, Robertson D, White S, Haynor D, and Siddiqui K (2011). Robust linear registration of CT images using random regression forests. In the SPIE Conference on Medical Imaging: Image Processing, SPIE, 7962: 1X-1-1X-8. 
  7. Pluim JP, Maintz JA, and Viergever MA (2000). Image registration by maximization of combined mutual information and gradient information. In the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer Berlin Heidelberg: 1935: 452-461. 
  8. Pluim JPW, Maintz JBA, and Viergever MA (2003). Mutual-informationbased registration of medical images: A survey. IEEE Transactions on Medical Imaging, 22(8): 986-1004.  PMid:12906253 
  9. Purwani S and Twining CJ (2014). Ensemble registration: Incorporating structural information into groupwise registration. In the International Conference on Visual Computing: Advances in Visual Computing, Springer: 8887: 41-50. 
  10. Sabuncu MR and Ramadge P (2008). Using spanning graphs for efficient image registration. IEEE Transactions on Image Processing, 17(5): 788-797. PMid:18390383     
  11. Shannon CE (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3): 379-423. 
  12. Sotiras A, Davatzikos C, and Paragios N (2013). Deformable medical image registration: A survey. IEEE Transactions on Medical Imaging, 32(7): 1153-1190.  PMid:23739795 PMCid:PMC3745275 
  13. Studholme C, Hill DL, and Hawkes DJ (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 32(1): 71-86. 
  14. Studholme D, Hill DLG, and Hawkes DJ (1995). Multiresolution voxel similarity measures for MR-PET registration. In: Bizais Y, Barillot C, and Di Paola R (Eds.), Information processing in medical imaging: 287-298. Kluwer, Dordrecht, The Netherlands. PMid:8924407     
  15. Tsao J (2003). Interpolation artifacts in multimodality image registration based on maximization of mutual information. IEEE Transactions on Medical Imaging, 22(7): 854-864.  PMid:12906239 
  16. Twining CJ and Taylor CJ (2011). Specificity: A graph-based estimator of divergence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12): 2492-2505.  PMid:21576742 
  17. Viola P and Wells III WM (1997). Alignment by maximization of mutual information. International journal of Computer Vision, 24(2): 137-154.
  18. Viola PA (1995). Alignment by maximization of mutual information. Ph.D. Dissertation, Massachusetts Institute of Technology, Massachusetts, USA. 
  19. Zitová B and Flusser J (2003). Image registration methods: A survey. Image and Vision Computing, 21(11): 977-1000.