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Median filtering has been widely used in image processing for noise removal because it can significantly reduce the power of noise while limiting edge blurring. This filtering is still a challenging task in the case of three-dimensional images containing up to a billion of voxels, especially for large size filtering windows. The authors encountered the problem when applying median filter to speckle noise reduction in optical coherence tomography images acquired by the Spark OCT systems. In the paper a new approach to the GPU (Graphics Processing Unit) based median smoothing has been proposed, which uses two-step evaluation of local intensity histograms stored in the shared memory of a graphic device. The solution is able to output about 50 million voxels per second while processing the neighbourhood of 125 voxels by Quadro K6000 graphic card configured on the Kepler architecture.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
25--34
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
- Lodz University of Technology, Institute of Applied Computer Science, Stefanowskiego 18/22, PL 90-924 Lodz, Poland
autor
- Lodz University of Technology, Institute of Applied Computer Science, Stefanowskiego 18/22, PL 90-924 Lodz, Poland
Bibliografia
- [1] Benzarti, F., Amiri, H. (2013). Speckle noise reduction in medical ultrasound images. International Journal of Computer Science Issues 9(3)
- [2] Chhablani, J., Krishnan, T., Sethi, V., Kozak, I. (2014). Artifacts in optical coherence tomography. Saudi Journal of Ophthalmology, 28(2), 81-87
- [3] Gonzalez, R. C., Woods, R. E. (1992). Digital imaging processing. Massachusetts: Addison-Wesley
- [4] Goodman, J.W. (1976). Some fundamental properties of speckle. JOSA, 66(11), 1145-1150
- [5] Jain, A.K. (1989). Fundamentals of digital image processing. Prentice-Hall, Inc.
- [6] Loupas, T., McDicken, W.N., Allan, P.L. (1987). Noise reduction in ultrasonic images by digital filtering. The British journal of radiology, 60(712), 389-392
- [7] Malik, K., Smołka, B. (2012). Improved bilateral filtering scheme for noise removal in color images. In The International Conference on Informatics and Applications (ICIA2012) (pp. 118-130). The Society of Digital Information and Wireless Communication
- [8] Malik, K., Machala, B., Smołka, B. (2015). Noise Reduction in Ultrasound Images Based on the Concept of Local Neighborhood Exploration. In Image Processing & Communications Challenges 6 (pp. 103-110). Springer International Publishing
- [9] Michailovich, O.V., Tannenbaum, A. (2006). Despeckling of medical ultrasound images. Ultrasonics, Ferroelectrics, and Frequency Control, IEEE Transactions on, 53(1), 64-78
- [10] Perrot, G., Domas, S., Couturier, R. (2014). Fine-tuned High-speed Implementation of a GPU-based Median Filter. Journal of Signal Processing Systems, 75(3), 185-190
- [11] Robinson, S. (2011). Field Mapping Toolbox. http://www.mathworks.com/matlabcentral/fileexchange/30853-field-mapping-toolbox/content/medfilt3.m
- [12] Sánchez, R.M., Rodríguez, P. (2012). Bidimensional median filter for parallel computing architectures. In Acoustics, speech and signal processing (ICASSP), 2012 IEEE International Conference on (pp. 1549-1552). IEEE
- [13] Sanders, J., Kandrot, E. (2010). CUDA by example: an introduction to general-purpose GPU programming. Addison-Wesley Professional.
- [14] Schmitt, J.M., Xiang, S.H., Yung, K.M. (1999). Speckle in optical coherence tomography. Journal of biomedical optics, 4(1), 95-105
- [15] Tukey, J.W. (1974). Nonlinear (nonsuperposable) methods for smoothing data. Congr. Rec. 1974 EASCON, 673
- [16] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P. (2004). Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on, 13(4), 600-612
- [17] Wasatch Photonics. (2015). Spark OCT system. http://wasatchphotonics.com/systems/optical-coherence-tomography/spark-oct-system/
- [18] Yu, Y., Acton, S.T. (2002). Speckle reducing anisotropic diffusion. Image Processing, IEEE Transactions on, 11(11), 1260-1270
- [19] Zhou, Z., Guo, Z., Dong, G., Sun, J., Zhang, D., Wu, B. (2015). A Doubly Degenerate Diffusion Model Based on the Gray Level Indicator for Multiplicative Noise Removal. Image Processing, IEEE Transactions on, 24(1), 249-260
Typ dokumentu
Bibliografia
Identyfikator YADDA
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