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A New Approach of Image Registration for Biomedical Images Using Saliency Information

Author Affiliations

  • 1 Department of Information and Communication Engineering, Anna University Chennai Regional Center Madurai Madurai, INDIA

Res. J. Engineering Sci., Volume 2, Issue (3), Pages 11-15, March,26 (2013)

Abstract

In this paper we propose a Markov Random Field based Automatic Registration method. This is an elastic registration method that uses the combination of saliency and gradient information. This Intensity-based registration of images is done by linear transformations, based on a discrete Markov random field (MRF) formulation. Here, the challenge arises from the fact that optimizing the energy associated with this problem requires a high-order MRF model. Currently, methods for optimizing such high-order models are less general, easier to use, and efficient, than methods for the popular second-order models. Automatic registration of dynamic contrast enhanced magnetic resonance (DCE-MR) images is a challenging task due to the rapid changes of the images which are characterized by intensity changes over time, thus posing challenges for conventional intensity-based registration methods. Saliency information contributes to a contrast invariant metric to identify similar regions inspiteof contrast enhancement. Saliency is used in optimization framework to identify relevant pixels for registration, thus reducing the computation time. Experimental results on real patient images demonstrate superior registration accuracy with a combination of saliency and gradient information over other similarity metrics.

References

  1. Glocker B., Komodakis N., Navab N., Tziritas G. and Paragios N., Dense image registration through MRFs and efficient linear programming, Med. Image Anal., 12(6),731–741 (2008)
  2. Boykov Y. and Veksler, Fast approximate energy minimization via graph cuts, IEEE Trans. Pattern Anal. Mach. Intell., 23(11), 1222–1239 (2001)
  3. Holden M., A review of geometric transformations for nonrigid body registration, IEEE Trans. Med. Imag., 27(1), 111–128 (2008)
  4. Itti L., Koch C. and Niebur. E, Model of saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal .Mach. Intell.,20(11), 1254–1259 (1998)
  5. Itti L. and Koch C., A saliency-based search mechanism for overt and covert shifts of visual attention, Vis. Res., 40,1489–1506 (2000)
  6. Kadir T. and Brady M., Saliency, scale and image description, Int. J. Comput. Vis., 45, 83–105 (2001)
  7. Rohde G., Aldroubi A. and Dawant B., The adaptive bases algorithm for intensity based nonrigid image registration, IEEE Trans. Med. Imag., 22(11), 1470–1479 (2003)
  8. Rueckert D., Sonoda I., Hayes C., Hill D.L.G., Leach M.O. and Hawkes D.J., Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imag., 18(8), 712–721 (1999)
  9. Shekhovtsov A., Kovtun I. and Hlav´ac V., Efficient MRF deformation model for non-rigid image matching, Comput.Vision Image Understand.,112(1), 91–99 (2008)
  10. SunY., Jolly M.P. and Moura J., Contrast-invariant registration of cardiac and renal MR perfusion images, in Proc. Med. Image Comput. Comput.-Assisted Intervention (MICCAI), 903–910 (2004)
  11. J.P., Image matching as a diffusion process: an analogy with Maxwell’s demons, Med. Image Anal.,2, 243–260 (1998)
  12. Woods R.P., Mazziotta J.C. and Cherry S.R., Mri-pet registration with automated algorithm, J. Comput. Assist. Tomogr., 17(4), 536–546 (1993)
  13. Dinkar A., Bhattacharyya S., Kumar D., Kumar A., Gupta P., Banerjee G.1 and Singh M., Pneumonia caused by Candida Kefyr ion pediatric patient with acute Lymphoblastic Leukaemia: Case Report”, Inernational Research journal of Engineering sciences, 1(1), 18-19 (2013)
  14. Uma R. and Pravin B, Invitro Cytotoxic Activity of Marsilea Quadrifolia Linn of MCF-7 Cells of Human Breast Cancer International Research Journal of Medical Sciences, 1(1), 10-13 (2013)
  15. Budi Setiyono, Mochamad Hariadi and Mauridhi Hery Purnomo, “Investigation of Superresolution using Phase based Image Matching with Function Fitting”, Research Journal Engineering Sciences, 1(3), 38-44 (2012)