International Journal of Computer
Trends and Technology

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Volume 4 | Issue 5 | Year 2013 | Article Id. IJCTT-V4I5P83 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I5P83

Biomedical Image Registration Using Fuzzy Logic

Himadri Nath Moulick, Anindita Chatterjee

Citation :

Himadri Nath Moulick, Anindita Chatterjee, "Biomedical Image Registration Using Fuzzy Logic," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 5, pp. 1394-1406, 2013. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V4I5P83

Abstract

Optimization of the similarity measure is an essential theme in medical image registration. In this paper, a novel continuous medical image registration approach (CMIR) is proposed. This is our extension work of the previous one where we did a segmentation part of any particular image with a custom algorithm .The CMIR, considering the feedback from users and their preferences on the trade-off between global registration and local registration, extracts the concerned region by user interaction and continuously optimizing the registration result. Experiment results show that CMIR is robust, and more effective compared with the basic optimization algorithm. Image registration, as a precondition of image fusion, has been a critical technique in clinical diagnosis. It can be classified into global registration and local registration. Global registration is used most frequently, which could give a good approximation in most cases and do not need to determine many parameters. Local registration can give detailed information about the concerned regions, which is the critical region in the image. Finding the maximum of the similarity measure is an essential problem in medical image registration. Our work is concentrating on that particular section with the synergy of Tpe-2 fuzzy logic invoked in it.

Keywords

Multi-model image alignment, Extrinsic method, intrinsic method Introduction.

References

[1] Fred L. Bookstein, Principal warps: Thin-plate splines and the decomposition of deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (1989), no. 6, 567–585.
[2] F. Brezzi and M. Fortin, Mixed and hybrid finite element methods, Springer, 1991.
[3] Morten Bro-Nielsen, Medical image registration and surgery simulation, Ph.D. thesis, MM, Technical University of Denmark, 1996.
[4] Chaim Broit, Optimal registration of deformed images, Ph.D. thesis, Computer and Information Science, Uni Pensylvania, 1981.
[5] Gary E. Christensen and H. J. Johnson, Consistent image registration, IEEE Transaction on Medical Imaging 20 (2001), no. 7, 568–582.
[6] Gary Edward Christensen, Deformable shape models for anatomy, Ph.D. thesis, Sever Institute of Technology, Washington University, 1994.
[7] A. Collignon, A. Vandermeulen, P. Suetens, and G. Marchal, 3d multimodality medical image registration based on information theory, Kluwer Academic Publishers: Computational Imaging and Vision 3 (1995), 263–274.
[8] R. Courant and David Hilbert, Methods of mathematical physiks, vol. II, Wiley, New York, 1962.
[9]  Bernd Fischer and Jan Modersitzki, Fast inversion of matrices arising in image processing, Num. Algo. 22 (1999), 1–11.
[10] Fast diffusion registration, AMS Contemporary Mathematics, Inverse Problems, Image Analysis, and Medical Imaging, vol. 313, 2002, pp. 117–129.
[11] Combination of automatic non-rigid and landmark based registration: the best of both worlds, Medical Imaging 2003: Image Processing (J.M. Fitzpatrick M. Sonka, ed.), Proceedings of the SPIE 5032, 2003, pp. 1037–1048.