Abstract
A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Kσ/σx, where σ and σx were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.
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References
Achim, A., Bezerianos, A., andTsakalides, P. (2001) ‘Novel Bayesian multiscale method for speckle removal in medical ultrasound images’,IEEE Trans. Med. Imag.,20, pp. 772–783
Arsenault, H. H., andApril, G. (1976): ‘Properties of speckle integrated with a finite aperture and logarithmically transformed’,J. Opt. Soc. Am.,66, 1160–1163
Chang, G., Yu B., andVetterli, M. (2000): ‘Spatially adaptive wavelet thresholding with context modelling for image denoising’,IEEE Trans. Image. Proc.,9, pp. 1522–1531
Chivers, R. C., Davies, I. J., andDuarte, F. M. (1986): ‘Perceptual studies and ultrasonic B-scam textures’,Phys. Med. Biol.,31, pp. 627–634
Donoho, D. L., andJohnstone, I. M. (1994): ‘Ideal spatial adaptation via wavelet shrinkage’,Biometrica,81, pp. 425–455
Donolho, D. L. (1995): ‘Denoising by soft-thresholding’,IEEE Trans. Inf. Theory,41, pp. 613–627
Donolho, D. L., andJohnstone, I. M. (1995): ‘Adapting to unknown smoothness via wavelet shrinkage’,J. Am. Stat. Assoc.,90, pp. 1200–1224
Ghofrani, S., Jahed-Motlagh, M. R., andAyotollahi, A. (2001): ‘An adaptive speckle suppression filter based on Nakagami distribution’ (IEEE, 2001)
Goodman, J. W. (1976): ‘Some fundamental properties of Speckle’,J. Opt. Soc. Am.,66, pp. 1145–1150
Guo, H., Odegard, M. L., Gopinath, R. A., Selesnick, I. W., andBurrus, C. S. (1994): ‘Wavelet based speckle reduction with application to SAR based ATD/R’,Proc. ICIP, Vol.1, pp. 75–79
Gupta, S., andKaur, L. (2002): ‘Wavelet based image compression using Daubechies filters’.8th Nat. Conf. on Communications, IIT, Bombay
Jain, A. K. (1989): ‘Fundamentals of digital image processing’ (Prentice Hall, Englewood Cliffs, NJ, 1989)
Jansene, M. (2001): ‘Noise reduction using wavelets, Lecture series in Statistics’, (Springer-Verlag, New York, 2001)
Joshi, R. L., Crump, V. J., andFisher, T. R. (1995): ‘Image subband coding using arithmetic and trellis coded quantization’,IEEE Trans. Circuits Syst. Video Technol.,5, pp. 515–523
Kremkau, F. W., andTaylor, K. J. W. (1986): ‘Artefacts in ultrasound imaging’,J. Ultrasound Med.,5, pp. 227–237
Loannidis, A., Kazakos, D., andWatson, D. D. (1984): ‘Application of median filtering on nuclear medicine scintigram images’,Proc. 7th Int. Conf. Pattern Recognition, pp. 33–36
Loupas, T., McDicken, W. N., andAllen, P. L. (1987): ‘Noise reduction in ultrasonic images by digital filtering’,Br. J. Radiol.,60, pp. 389–392
Lowe, H., Bamber, J. C., Webb, S., andCook-Martin, G. (1988): ‘Perceptual studies of contrast, texture and detail in ultrasound B-scans’,SPIE Proc.,914, pp. 40–47
Mallat, S. (1989): ‘A theory for multiresolution signal decomposition: the wavelet representation’,IEEE Trans. Patterns Anal. Mach. Intell.,11, pp. 674–692
Mallat, S., (1998): ‘A wavelet tour of signal processing’ (Academic Press, New York, 1998)
Ritenour, E. R., Nelson, T. R., andRaff, U. (1984): ‘Application of median filter to digital readiographic images’.Proc. 7th Int. Conf. Acoust. Speech, Signal Processing, pp. 23.1.1–23.1.4
Sattar, F., Floreby, L., Salomonsson, G., andLovstrom, B. (1997): ‘Image enhancement based on a nonlinear multiscale method’,IEEE Trans. Image Proc.,6, pp. 888–895
Simoncelli, E. P., andAdelson, E. H. (1996): ‘Noise removal via Bayesian wavelet coring’,Third Int. Conf. Image Processing,1, pp. 379–382
Smith, S. W., andLopez, H. (1982): ‘A contrast detail analysis of diagnostic ultrasound imaging’,Med. Phys.,9, pp. 4–12
Stein, C. M. (1981): ‘Estimation of the mean of a multivariate normal distribution’,Ann. Statist.,9, pp. 1135–1151
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Gupta, S., Chauhan, R.C. & Sexana, S.C. Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med. Biol. Eng. Comput. 42, 189–192 (2004). https://doi.org/10.1007/BF02344630
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DOI: https://doi.org/10.1007/BF02344630