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A Novel Approach for Fusion of Multimodality Medical Images

Dolly Garg, Pankaj Bhambri

Abstract


Multi modal medical image fusion has a great contribution in the field of medical imaging and has proved helpful in the field of clinical diagnosis. In this paper, a novel discrete wavelet transform (DWT) based approach for fusion of multi modal medical images is presented which is developed by taking into account the characteristics of Human Visual System (HVS) and also the physical meaning of the wavelet coefficients. Besides this some existing well-known image fusion methods are discussed. Image fusion aims to generate a new image by combining two or more images in order to produce a more informative and clear fused image. The image fusion can be performed using pixel or region based methods. The images are first decomposed and then different fusion rules are applied on the low frequency and high frequency bands depending upon the requirement of the fusion to be done. After the fused coefficients have been obtained the inverse discrete wavelet transform (IDWT) is applied to obtain the fused image.

Keywords


Image Fusion, Wavelet Transform, Pixel Based Image Fusion, Region Based Image Fusion, Multimodal Medical Images, Human Visual System

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References


M. Sasikala and N. Kumaravel, ―A comparative analysis of featurebased image fusion methods,‖ Information Technology Journal,6(8):1224- 1230, 2007.

Guihong Qu, Dali Zhang and Pingfan Yan - Medical image fusion by wavelet transform modulus maxima, OPTICS EXPRESS, Vol. 9, No. 4 , 2001.

R.SABARI BANU, ―Medical Image Fusion by the Analysis of Pixel Level Multi-sensor Using Discrete Wavelet Transform‖ Proceedings of the National Conference on Emerging Trends in Computing Science ―NCETCS 2011.

A. Wang, H. Sun and Y. Guan, ―The application of wavelet transform to multimodality medical image fusion,‖ Proc. IEEE International Conference on Networking, Sensing and Control (ICNSC), Ft. Lauderdale, Florida, 2006, pp.270-274.

O. Rockinger, ―Pixel-level fusion of image sequences using wavelet frames,‖ Proc. of the 16th Leeds Applied Shape Research Workshop, Leeds University Press, 1996, 149-154.

H. Li, B. S. Manjunath, S. K. Mitra, ―Multisensor image fusion using the wavelet transform,‖ Graphical Models and Image Processing, 57(3):235-245, May 1995.

M. Jian, J. Dong and Y. Zhang, ―Image fusion based on wavelet transform,‖ Proc., 8th ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Distributed Computing,,Qingdao, China, July 2007.

Z. Yingjie and G. Liling, ―Region-based image fusion approach using iterative algorithm,‖ Proc. Seventh IEEE/ACIS International conference on Computer and Information Science(ICIS), Oregon, USA, May 2008.

H. Zhang, L. Liu and N. Lin, ―A novel wavelet medical image fusion method,‖ International Conference on Multimedia and Ubiquitous Engineering (MUE’07), Seoul, Korea, April 2007.

V. Petrovic, ―Multilevel image fusion,‖ Proceedings of SPIE, 5099:87- 96, 2003.

Y. Zheng, X. Hou, T. Bian and Z. Qin, ―Effective image fusion rules of multiscale image decomposition,‖ Proc. of 5th International Symposium on Image and Signal Processing and Analysis (ISPA07), Istanbul, Turkey, September 2007, pp. 362-366.

J. Gao, Z. Liu and T. Ren, ―A new image fusion scheme based on wavelet transform,‖ Proc., 3rd International Conference on Innovative Computing, Information and Control, Dalian, China, June 2008.

V. S. Petrovic and C. S. Xydeas, ―Gradient-based multiresolution image fusion,‖ IEEE Transactions on Image Processing, vol. 13, no. 2, pp. 228–237, 2004.

Z. Zhang and R. S. Blum, ―A categorization of multiscale decomposition- based image fusion schemes with a performance study for a digital camera application,‖ Proceedings of the IEEE, vol. 87, no. 8, pp. 1315–1326, 1999.

Xiaoqing Zhang, Yongguo Zheng, Yanjun Penga, Weike Liub, Changqiang Yang, ―Research on multi-mode medical image fusionalgorithm based on wavelet transform and the edgecharacteristics of images‖, Proceedings of the IEEE, 978-1-4244-4131-0/09, 2009.

Susmitha Vekkot, and Pancham Shukla, ―A Novel Architecture for Wavelet based Image Fusion‖, World Academy of Science, Engineering and Technology 57 2009.

Yong Yang, ―Multimodal Medical Image Fusion Through a New DWT Based Technique‖, Proceedings of the IEEE, 978-1-4244-4713-8/10, 2010.

Yong Yang and Shuying Huang, ―Fusion of CT & MR Images with a Novel Method Based on Wavelet Transform‖, 978-1-4244-2902-8, IEEE, 2009.

Gonzalo Pajares, Jes*us Manuel de la Cruz, ―A wavelet-based image fusion tutorial‖, Pattern Recognition Society, 2004.

Yang Sa, ―Application of Multi-wavelet Transform in Multifocus Image Fusion‖, First International Workshop on Education Technology and Computer Science, 2009.

CHENG Shangli, HE Junmin, Lv Zhongwei, ―Medical Image of PET/CT Weighted Fusion Based on Wavelet Transform‖, 978-1-4244-1748-3/08,IEEE ,2008.

Yong Yang, Dong Sun Park, Shuying Huang and Nini Rao, ―Medical Image Fusion via an EffectiveWavelet-Based Approach‖, EURASIP Journal on Advances in Signal Processing, 2010.

Yuhui Liu, Jinzhu Yang and Jinshan Sun, ―PET/CT Medical Image Fusion Algorithm Based on Multiwavelet Transform‖, 978-1-4244-5848-6/10, IEEE, 2010.

ZHANG Bin, ―Study on Image Fusion Based on Different Fusion Rules of Wavelet Transform‖, 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 2010.

Hai-Hui Wang, ― Multisensor Image Fusion By Using Discrete Multiwavelet Transform‖, b e d i n g s of the Third International Conference on Machine Learning and Cybernetics, 2004.

Yong Yang, Dong Sun Park , Shuying Huang, Zhijun Fang, Zhengyou Wang, ―Wavelet based Approach for Fusing Computed Tomography and Magnetic Resonance Images‖, 978-1-4244-2723-9/09,IEEE,2009.

Zhu Shu-long, ―Image Fusion Using Wavelet Transform‖, Symposium on Geospatial Theory, Processing and Applications,2002.

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