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International Journal of Technology Enhancements and Emerging Engineering Research (ISSN 2347-4289)
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IJTEEE >> Volume 3 - Issue 7, July 2015 Edition



International Journal of Technology Enhancements and Emerging Engineering Research  
International Journal of Technology Enhancements and Emerging Engineering Research

Website: http://www.ijteee.org

ISSN 2347-4289



Hybrid PCA-DCT Based Image Fusion For Medical Images

[Full Text]

 

AUTHOR(S)

Prabhdip Kaur

 

KEYWORDS

Keywords: Image Fusion, PCA, DCT.

 

ABSTRACT

ABSTRACT: The purpose of image fusion is to merge relevant information from multiple images right into a single image. In this paper, by conducting the review it has been discovered that the majority of the existing techniques are based upon transform domain therefore it could results in some artifacts which might decrease the execution of the transform based vision fusion techniques. Moreover it is already been discovered that the issue of the uneven illuminate has already been neglected in the absolute most of existing focus on fusion. Therefore to overcome these issues, a fresh method which integrates the larger valued Alternating Current (AC) coefficients calculated in iterative block level principal component averaging (IBLPCA) domain base fusion with illuminate normalization and fuzzy enhancement has been proposed in this paper. The experimental results show the efficiency of proposed algorithm over existing work.

 

REFERENCES

[1] D. Agrawal J. Singhai “Multifocus image fusion using modified pulse coupled neural network for improved image quality” IET Image Process., 2010, Vol. 4, Iss. 6, pp. 443–451

[2] Li, Shutao, Haitao Yin, and Leyuan Fang. "Remote sensing image fusion via sparse representations over learned dictionaries." Geoscience and Remote Sensing, IEEE Transactions on 51, no. 9 (2013): 4779-4789.

[3] Ghimire, Deepak and Joonwhoan Lee“Nonlinear Transfer Function-Based Local Approach for Color Image Enhancement.” In Consumer Electronics, 2011 International Conference on, pp. 858-865. IEEE,2011

[4] Gintautas Palubinskas and Peter Reinartz. “Multi-resolution, multi-sensor image fusion:general fusion framework.” In Joint Urban Remote Sensing Event, 2011 International Conference on, pp. 313-316. IEEE, 2011.

[5] Alex Pappachen James , Belur V. Dasarathy “Medical image fusion: A survey of the state of the art” Information Fusion 19 (2014) 4–19.

[6] Y.AsnathVictyPhamila, R.Amutha. “Discrete Cosine Transform based fusion of multi-focus images for visual sensor networks.” In Signal Processing, 2013 International Conference on, pp.161-170. IEEE, 2013.

[7] R. Vijayarajan, S. Muttan “Iterative block level principal component averaging medical imagefusion” Optik 125 (2014) 4751–4757

[8] Yang, Cui, Jian-Qi Zhang, Xiao-Rui Wang, and Xin Liu. "A novel similarity based quality metric for image fusion." Information Fusion 9, no. 2 (2008): 156-160.

[9] Han, Dong, Zhenhua Guo, and David Zhang. "Multispectral palmprint recognition using wavelet-based image fusion." In Signal Processing, 2008. ICSP 2008. 9th International Conference on, pp. 2074-2077. IEEE, 2008.

[10] Yang, L., B. L. Guo, and W. Ni. "Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform." Neurocomputing 72, no. 1 (2008): 203-211.

[11] Zhang, Yingjie, and Liling Ge. "Efficient fusion scheme for multi-focus images by using blurring measure." Digital signal processing 19, no. 2 (2009): 186-193.

[12] Yang, Bin, and Shutao Li. "Pixel-level image fusion with simultaneous orthogonal matching pursuit." Information Fusion 13, no. 1 (2012): 10-19.

[13] Wang, Zhaobin, Yide Ma, and Jason Gu. "Multi-focus image fusion using PCNN." Pattern Recognition 43.6 (2010): 2003-2016.

[14] Daneshvar, Sabalan, and Hassan Ghassemian. "MRI and PET image fusion by combining IHS and retina-inspired models." Information Fusion 11, no. 2 (2010): 114-123.

[15] Li, Shutao, and Bin Yang. "Hybrid multiresolution method for multisensor multimodal image fusion." Sensors Journal, IEEE 10, no. 9 (2010): 1519-1526.

[16] Tian, Jing, and Li Chen. "Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure." Signal Processing 92, no. 9 (2012): 2137-2146.

[17] Liu, Zheng, Erik Blasch, Zhiyun Xue, Jiying Zhao, Robert Laganiere, and Wei Wu. "Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study." Pattern Analysis and Machine Intelligence, IEEE Transactions on 34, no. 1 (2012): 94-109.

[18] Xiaoyan Luo a,n, JunZhang a,n, QionghaiDai b a A regional image fusion based on similarity characteristics Signal Processing 92 (2012) 1268–1280.

[19] Zhao, Hengjun, Zhaowei Shang, Yuan Yan Tang, and Bin Fang. "Multi-focus image fusion based on the neighbor distance." Pattern recognition 46, no. 3 (2013): 1002-1011.