Open Access
Subscription Access
Open Access
Subscription Access
An Adaptive Fusion of Noisy Images for Multi Modal Medical Image Application
Subscribe/Renew Journal
Multimodal medical images lends to certain problems in the medical imaging fusion system, since the devices introduces noise during the process of image capturing. Because of the appearance of noises in the input images, it leads to trouble as introducing artifacts in the resulted image of fusing the degraded images. This paper proposes a fusion approach for noisy images, captured from two different modalities, that merge adaptively unique details of the images denoised by i) bilateral method ii) curvelet method iii) 2D modulated filter bank method. For the fusion of denoised images, weight may be calculated by residuals of the recommended filtering methods. Performance measures required to show the stability of the proposed method are effectively employed in comparing the results with other state-of-the-art fusion methods.
Keywords
Fusion, Residual, Bilateral, Curvelet, Denoise, Filter Bank.
Subscription
Login to verify subscription
User
Font Size
Information
- Jens, B. & Felix, N. (2008). Data Fusion. Journal of ACM Computing Surveys, 41(1),1-40.
- Shutao, Li.,Haitao, Y. & Leyuan, F. (2012). Groupsparse Representation with Dictionary Learning for Medical Image Denoising and Fusion. IEEE Transactions on Biomedical Engineering, 59(12), 3450-3459.
- Hall, D. L. & James, L. (1997). An Introduction to Multisensor Data Fusion. Proceedings of the IEEE, 85(1),6-23.
- Porter, B. C., Rubens, D. J., Strang, J. G., Smith, J., Totterman, S. & Parker, K. J. (2001). Threedimensional registration and fusion of ultrasound and MRI using major vessels as fiducial markers. IEEE Transactions on Medical Imaging, 20(4), 354-359.
- Toet, A. (1990). Hierarchical image fusion. Journal of Machine Vision and Applications, 3(1), 1-11.
- Petrovic, V. S. & Xydeas, C. S. (2004). Gradientbased Multi-resolution Image Fusion. IEEE Transactions on Image Processing, 13(2), 228-237.
- Gravel, P., Beaudoin, G. D. & Guise, J. A. (2004). A Method for Modeling Noise in Medical Images. IEEE Transactions on Medical Imaging, 23(10),21-32.
- Rabbani,H., Nezafat, R. & Gazor, S. (2009). Waveletdomain Medical Image Denoising using Bivariate Laplacian Mixture Model. IEEE Transactions on Biomedical Engineering, 56(12), 2826-2837.
- Lin, J, W., Laine, A. F. & Bergmann, S. R. (2001). Improving PET-based Physiological Quantification through Methods of Wavelet Denoising. IEEE Transactions on Biomedical Engineering, 48(2), 202-212.
- Drapaca, C. S. (2009). A Nonlinear Total Variationbased Denoising Method with Two Regularization Parameters. IEEE Transactions on Biomedical Engineering, 56(3), 582-586.
- Oster, J., Pietquin, O., Kraemer, M. & Felblinger, J. (2010). Nonlinear Bayesian Filtering for Denoising of Electrocardiograms Acquired in a Magnetic Resonance Environment. IEEE Transactions on Biomedical Engineering, 57(7), 1628-1638.
- Dabov, K., Foi, A.,Katkovnik, V. & Egiazarian, K. (2007). Image Denoising by Sparse 3-D Transformdomain Collaborative Filtering. IEEE Transactions Image Process, August, 16(8), 2080-2095.
- Coup'e, P., Yger, P., Prima, S., Hellier, P.,Kervrann, C. & Barillot, C. (2008). An Optimized Blockwise Non-local means Denoising Filter for 3-D Magnetic Resonance Images. IEEE Transactions on Medical Imaging, 27(4), 425-441.
- Bhadauria, H. S. & Dewal, M. L. (2013). Medical image denoising using adaptive fusion of curvelet transform and total variation. Computers and Electrical Engineering, 39(5), 1451-1460.
- Angelini., E., Jin, D., Yinpeng, E., Peter, D., Heertum, V., Laine, R. L. & Andrew, F. (2004). Fusion of Brushlet and Wavelet Denoising Methods for Nuclear Images. 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano, April, 2, 1187-1191.
- Paris, S., Kornprobst, P., Tumblin, J. & Durand, F.: 'Bilateral Filtering: Theory and Applications’. Foundations and Trends R in Computer Graphics and Vision., 4(1), 1-73.
- Wen-Chung., K. & Ying-Ju, C. Multistage Bilateral Noise Filtering and Edge Detection for Color Image Enhancement. IEEE Transactions on Consumer Electronics, 51(4), 1346-1351.
- Ali,F. E., El-Dokany, I. M., Saad, A. A. & El-Samie, F. E. (2010). A curvelet transform approach for the fusion of MR and CT images. Journal of Modern Optics, 57(4), 273-286.
- Strack, J. L., Candes, E. J. & Donoho,D. L. (2000). The Curvelet Transform for Image Denoising. IEEE Transactions on Image Processing, 11(6), 670-684.
- Shui, P. L. (2009). Image Denoising using 2-D Separable Oversampled DFT Modulated Filter Banks. IET Image Processing, 2009, 3(3), 163-173.
- Tomasi, C. & Manduchi, R. (1998). Bilateral Filtering for Gray and Color Images. Proceedings of the 6th International Conference on Computer Vision, (pp. 839-846).
- Candes, E. J. & Donoho, D. L. (2000). Curvelets, Multi-resolution Representations, and Scaling Laws. Proceedings of Wavelet Applications Signal Image Processing, 4419, pp. 1-12.
- Bamberger, R. H. & Smith, M. J. T. (1992). A Filter Bank for Directional Decomposition of Images: Theory and Design. IEEE Transactions on Signal Process, 40(4), 882-893.
- Nguyen, T. T. & Oraintara, S. (2005). Multiresolution Directional Filter Banks: Theory, Design, and Application. IEEE Transactions on Signal Processing, 53(10), 3895-3905.
- Singh, R. & Khare, A. Fusion of multimodal medical images using Daubechies complex wavelet transform - A multiresolution approach'. Information Fusion, http://dx.doi.org/10.1016/j.inffus.2012.09.005.
- Mumford, D. & Shah, J. (1989). 'Optimal approximations by piecewise smooth functions and associated variational problems'. Comm.Pure Appl. Math., 1989, 42(5), 577-685.
- Qu, G., Zhang, D. & Yan, P. (2002). Information measure for performance of image fusion. Electronic Letters, 38(7), 313-315.
- Sheikh, H. R. & Bovik, A. C. (2006). Image Information and Visual Quality. IEEE Transactions on Image Processing, 15(2), 430-444.
- Shutao, L., Bin, Y. & Jianwen, H. (2011). Performance comparison of different multi-resolution transforms for image fusion. Information Fusion, 12(2), 74-84.
Abstract Views: 467
PDF Views: 2