Robust non-homomorphic approach for speckle reduction in medical ultrasound images |
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Authors: | Dr S Gupta R C Chauhan S C Saxena |
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Institution: | (1) Sant Longowal Institute of Engineering & Technology, Longowal, India;(2) Thapar Institute of Engineering & Technology, Patiala, India |
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Abstract: | Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to
speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform
(homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering
is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads
to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks,
an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true
characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled
using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering
conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the
Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor
used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results
showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art
despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR
of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method
suppressed the speckle noise well, while preserving the texture and organ surfaces. |
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Keywords: | Ultrasound Redundant discrete wavelet transform Generalised Nakagami distribution Generalised Gaussian distribution MAP estimator Speckle suppression |
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