Abstract
This paper presents a curvelet-based approach on the image denoising in magnetic resonance imaging (MRI). The method is worth of examination, because it has not been tested so far in case of MRI. The results show how the Digital Curvelet Transform method can be used for the noise reduction. The analysis of the Signal to Noise Ratio (SNR), Normal to Mean value (NM) and edge detection quality is applied. The Digital Curvelet Transform application provides additional possibilities like image compression and image fusion, which could be also useful in the MRI application.
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References
Landini, L., Positano, V.: Advanced Image Processing in Magnetic Resonance Imaging. CRC Press (2005)
Starck, J.L., Fionn, M.: Sparse Image and Signal Processing. Wavelets, Curvelets, Morphological Diversity. Cambridge University Press (2010)
Candes, E., Donoho, D.: Curvelets–a surprisingly effective nonadaptive representation for objects with edges, pp. 105–120. Vanderbilt University Press, Nashville (2000)
Starck, J.L., Candes, E., Donoho, D.: The Curvelet Transform for Image Denoising. IEEE Transactions on Image Processing 11(6) (2002)
Jagadeeswar, G., Jayachandraprasad, T.: Curvelets with New Quantizer for Image Compression Global. Journal of Computer Science and Technology 12(8) (April 2012)
Parlewar, P.K., Bhurchandi, K.M.: A 4-quadrant curvelet transform for denoising digital images. International Journal of Automation and Computing 10(3), 217–226 (2013)
Candes, D., Guo, F.: New Multiscale Transforms, Minimum Total Variation Synthesis: Applications to Edge-Preserving Image Reconstruction. Signal Processing 82(11), 1519–1543 (2001)
Donoho, D., Duncan, M.: Digital Curvelet Transform: Strategy, Implementation and Experiments. Technical Report, Stanford University (1999)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing using Matlab. Pearson Education Inc., NJ (2004)
Świebocka-Więk, J.: Effectiveness analysis of selected noise reduction algorithms in magnetic resonance imaging. PhD thesis, AGH University of Science and Technology, Cracov (2013)
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© 2014 Springer International Publishing Switzerland
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Świebocka-Więk, J., Figiel, H. (2014). Application of the Digital Curvelet Transform for the Purpose of Image Denoising in MRI. In: Piętka, E., Kawa, J., Wieclawek, W. (eds) Information Technologies in Biomedicine, Volume 3. Advances in Intelligent Systems and Computing, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-319-06593-9_15
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DOI: https://doi.org/10.1007/978-3-319-06593-9_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06592-2
Online ISBN: 978-3-319-06593-9
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