FPGA Implementation of Super Resolution and Denoising of Magnetic Resonance Image Using Cubic Spline Interpolation

M. Satheesh*, Sabarinathan E.**, E. Manoj***
* Research Scholar, C.M.S. Group of Institutions, Namakkal, Tamilnadu, India.
** Lecturer, Department of Electrical and Electronics Engineering, C.M.S. Group of Institutions, Namakkal, Tamilnadu, India.
*** UG Scholar, Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, (Autonomous), Coimbatore, Tamilnadu, India.
Periodicity:June - August'2016
DOI : https://doi.org/10.26634/jpr.3.2.8266

Abstract

Image processing is the emerging field where it is possible to increase or decrease the quality of image without affecting the required features or objects in the image for particular applications. It includes the fields of medical, astronomy, satellites, etc., particularly in the digital medical imaging technologies such as Computerized Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), as well as combined modalities, e.g. SPECT/CT has revolutionized modern medicine. Resolutions of MRI medical images are very much intended for diagnosis and curing of physical and metabolic activities of the volume below the skin. Many image processing techniques for increasing the MRI image resolution are proposed which are not suitable for noise corrupted images. To provide solution for this, Super- Resolution techniques are introduced, all the Super-Resolution techniques are based on multiple images. After that, sparse representation techniques are proposed which are the single image super resolution methods, but it is based on the constructed databases, which require large computational tasks, and also it is a time consuming process. The proposed method contains only a single image which can be super resolved and denoised by the different techniques with increased PSNR (Peak Signal to Noise Ratio) and also compromising SSIM (Structural Similarity) with better visibility objects in the MRI image.

Keywords

Cubic Spline Interpolation, Image Denoising, Switched Bilateral Filter (SBF), Super Resolution, Field Programmable Gate Array (FPGA)

How to Cite this Article?

Satheesh, M., Sabarinathan, E., and Manoj, E. (2016). FPGA Implementation of Super Resolution and Denoising of Magnetic Resonance Image Using Cubic Spline Interpolation. i-manager’s Journal on Pattern Recognition, 3(2), 30-38. https://doi.org/10.26634/jpr.3.2.8266

References

[1]. Ali Gholipour, Judy Estroff, and, Simon Wareld, (2010). “Robust Super-Resolution Volume Reconstruction from Slice Acquisitions: Application to Fetal Brain MRI”. IEEE Transactions on Medical Imaging, Vol. 29, No. 10, pp. 1739-1758.
[2]. Bailey, P., Roach, E., Bailey, J., Hewlett, and Keijzers, (2007). “Development of a Cost-effective Modular SPECT/CT Scanner”. Eur. J. Nuclear Medical Molecular Images, Vol. 34, No. 9, pp. 1415–1426.
[3]. Ben-Eliezer, M., Irani, and Frydman, (2010). “Superresolved Spatially Encoded Single-scan 2D MRI”. Magnetic Resonance imaging, Vol. 63, No. 6, pp. 1594–1600.
[4]. Chih Hsing Lin, Jia Shiuan Tsai, and Ching Te Chiu, (2010). “Switching Bilateral Filter with a Texture/Noise Detector for Universal Noise Removal”. IEEE Transactions on Image Processing, Vol. 19, No. 9, pp. 2307-2320.
[5]. Dinh-Hoan Trinh, Marie Luong, Françoise Dibos, Jean-Marie Rocchisani, Canh-Duong, Truong Q., and Nguyen, (2014). “Novel Example-Based Method for Super-Resolution and Denoising of Medical Images”. IEEE Transactions on Image Processing, Vol. 23, No. 4, pp. 1882-1895.
[6]. Dougherty, (2009). Digital Image Processing for Medical Applications. In: Cambridge University Press.
[7]. Gholipour, Estroffand, and Warfield, (2010). “Robust Super-resolution Volume Reconstruction from Slice Acquisitions: Application to Fetal Brain MRI”. IEEE Transactions on Medical Images, Vol. 29, No. 10, pp. 1739–1758.
[8]. Elad, and Aharon, (2006). “Image Denoising via Sparse and Redundant Representations over Learned Dictionaries”. IEEE Transactions on Image Processing, Vol. 15, No. 12, pp. 3736–3745.
[9]. Hisehs, Harry C., and Andrews, (1978). “Cubic Splines for Image Interpolation and Digital Filtering”. IEEE Transactions on Image Processing, Vol. 26, No. 6, pp. 508- 517.
[10]. Jianchao Yang, John Wright, Thomas Huang, and Yi Ma, (2012). “Image Super-Resolution via Sparse Representation”. IEEE International Conference on Computational Intelligence and Computing Research, Vol. 8, No. 6, pp. 1-12.
[11]. Jianchao Yang, Zhaowen Wang, Zhe Lin, Scott Cohen, and Thomas Huang, (2012). “Coupled Dictionary Training for Image Super-Resolution”. IEEE Transactions on Image Processing, Vol 21, No. 8, pp. 3467-3478.
[12]. Kanwaljot Singh Sidhu, Baljeet Singh Khaira, and Ishpreet Singh Virk, (2012). “Medical Image Denoising In the Wavelet Domain Using Haar and DB3 Filtering”. International Refereed Journal of Engineering and Science (IRJES), Vol. 1, No. 1, pp. 1-8.
[13]. Kouam, and Ploquin, (2009). “Super-resolution in Medical Imaging: An Illustrative Approach through Ultrasound”. IEEE International Conference on Image Processing, Vol. 4, No. 8, pp. 249-252.
[14]. Liyakathunisa, and Ravi Kumar, (2011). “A Novel Super Resolution Reconstruction of Low Reoslution Images Progressively using DCT and Zonal Filter based Denoising”. International Journal of Computer Science & Information Technology (IJCSIT), Vol. 3, No. 1.
[15]. Min-Chun Yang, and Yu-Chiang Frank Wang, (2013). “A Self-Learning Approach to Single Image Super- Resolution”. Proceedings of the IEEE, Vol. 15, No. 3, pp. 498-508.
[16]. Nagaprudhviraj, and Venkateswarlu, (2013). “Denoising of MR Images using Adaptive Multi Resolution Sub Band Mixing”. IEEE International Conference on Computational Intelligence and Computing Research, Vol. 7, no. 4, pp. 345-356.
[17]. Wallach, (2007). “Super-resolution in 4D Positron Emission Tomography”. Proceedings of IEEE Nuclear Science Symposium Conference, pp. 4285–4287.
[18]. Wang, Bovik, Sheikh, and Simoncelli, (2004). “Image Quality Assessment: From Error Visibility to Structural Similarity ”. IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600–612.
[19]. Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, (2011). “Image De-blurring and Super- Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization”. IEEE Transactions on Image Processing, Vol. 20, No. 7, pp. 1838-1857.
[20]. Yaniv Galandrew, Mehnert Andrew, Bradley, Kerry McMahon, Dominic Kennedy, and Stuart Crozier, (2010). “Denoising of Dynamic Contrast-Enhanced MR Images using Dynamic Nonlocal Means”. IEEE Transactions on Medical Imaging, Vol. 29, No. 2, pp. 302-310.
[21]. Zhang Xiang-Guang, (2008). “A New Kind of Super- Resolution Reconstruction Algorithm based on the ICM and the Constrained Cubic Spline Interpolation”. Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 530-534.
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