PERFORMANCE OF WAVELET BASED MEDICAL IMAGE FUSION ON FPGA USING HIGH LEVEL LANGUAGE C
DOI:
https://doi.org/10.11113/jt.v76.5888Keywords:
Morphology, Matlab, FPGAs, image processingAbstract
In this paper presents the implementation of wavelet based medical image fusion on FPGA is performed using high level language C. The high- level instruction set of the image processor is based on the operation of image algebra like convolution, additive max-min, and multiplicative max-min. The above parameters are used to increase the speed. The FPGA based microprocessor is used to accelerate the extraction of texture features and high level C programming language is used for hardware design. This proposed hardware architecture reduces the hardware utilizations and best suitable for low power applications. The paper describes the programming interface of the user and outlines the approach for generating FPGA architectures dynamically for the image co-processor. It also presents sample implementation results (speed, area) for different neighborhood operations.
References
Arunmozhi, R. and G. Mohan. 2013. Wavelet-based Digital Image Fusion on Reconfigurable Fpga Using Handel-C Language. Int. J. Electron. Commun. Comput. Eng. 4: 1230-1234.
Besiris, D. and V. Tsagaris. 2012. An FPGA-based Hardware Implementation of Configurable Pixel-level Color Image Fusion. IEEE Trans. Geosci. Remote Sens. 50: 362-373. DOI: 10.1109/TGRS.2011.2163723.
Bhatnagar, G., Q. M. J. Wu and Z. Liu, 2013. Directive Contrast Based Multimodal Medical Image Fusion in NSCT Domain. IEEE Trans. Multimedia. 15: 1014-1024. DOI: 10.1109/TMM.2013.2244870.
Chen, S. L., H. Y. Huang and C. H. Luo. 2011. A Low-cost High-Quality Adaptive Scalar for Real-Time Multimedia Applications. IEEE Trans. Circuits Syst. Video Technol. 21: 1600-1611. DOI: 10.1109/TCSVT.2011.2129790.
Gonzalez, C., S. Sanchez, A. Paz, J. Resano and D. Mozos et al. 2013. Use of FPGA or GPU-based Architectures for Remotely Sensed Hyperspectral Image Processing. Integrat. VLSI J. 46: 89-103. DOI: 10.1016/j.vlsi.2012.04.002.
S. L. Chen, H. Y. Huang, and C. H. Luo. 2011. A low-cost High-quality Adaptive Scalar for Realtime Multimedia Applications. IEEE Trans. Circuits Syst. Video Technol. 21: 1600-1611.
D. Besiris and V. Tsagaris. 2012. An FPGA-Based Hardware Implementation of Configurable Pixel-Level Color Image Fusion. IEEE Trans. Geosci. Remote Sens. 50: 362-373.
N. Jacobson, M. Gupta, and J. Cole. 2007. Linear Fusion of Image Sets for Display. IEEE Trans. Geosci. Remote Sens. 45: 3277-3288.
K. Nagarajan, C. Krekeler, K. C. Slatton, and W. D. Graham. 2010. A Scalable Approach to Fusing Spatiotemporal Data to Estimate Streamflow via a Bayesian Network. IEEE Trans. Geosci. Remote Sens. 48: 3720-3732.
S. Li, H. Yin, and L. Fang. 2012. Group-Sparse representation with Dictionary Learning for Medical Image Denoising and Fusion. IEEE Transactions on Biomedical Engineering. 59: 3450-3459.
R. Arunmozhi and G. Mohan. 2013. Wavelet-Based Digital Image Fusion on Reconfigurable FPGA Using Handel-C Language. International Journal of Electronics Communication and Computer Engineering. 4(4): 2278-4209, ISSN Online): 2249–071X, ISSN (Print).
C. Gonzalez, S. Sanchez, Abel Paz, J. Resano, D. Mozos and A. Plaza. 2013. Use of FPGA or GPU-based Architectures For Remotely Sensed Hyperspectral Image Processing. Integration. The VLSI Journal. 46.
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