Robust non-homomorphic approach for speckle reduction in medical ultrasound images

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 0.42dB over the best state-of-the-art despeckling method reported in the literature, 1.73dB over the Lee filter and 1.31dB over the Kaun filter at an input SNR of 12.0dB, 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.

Comparing Non-homomorphic and Homomorphic Wavelet Filtering Techniques for Speckled Images

“…In soft thresholding, wavelet coefficients whose absolute values are lower than the threshold are set to zero, otherwise this method shrinks them toward zero (2) Although soft thresholding eliminates the discontinuity that is inherent in hard thresholding, the efforts to find optimum thresholding method are underway [13].…”

“…Beside these advantages, the disadvantage of the ultrasonic imaging is the poor image quality, which is affected by speckle noise. Other types of coherent imagery such as synthetic aperture radar and laser-illuminated imagery have speckle noise [2]. When the ultrasonic beam is scattered by microscopic structure, speckle occurs in the image as a granular pattern [3].…”

“…After wavelet filtering, exponential operation is performed. However, these operations cause biased estimation of the signal and increase the computational complexity [2]. This paper compares the non-homomorphic and the homomorphic wavelet filtering techniques for the speckled images.…”

Application of ultrasound in medicine part ii: the ultrasonic transducer and its associated electronics

“…Finally, an exponential operation is undertaken, which may lead to a biased estimation of the signal, increasing the computational complexity. This speckle can be reduced by the use of nonhomomorphic techniques [101], or sharpening enhancement using the contourlet transform [102] [103].…”

Artifact removal and texture-based rendering for visualization of 3D fetal ultrasound images

“…Although the quality of surface rendering is more aesthetically pleasing, using splatting techniques generally shifts the boundary location so that the shape is somewhat distorted. In fact, good image quality is essential in visualization, and despeckling (i.e., filtering that removes speckle noise) is helpful to alleviate this situation [3,4,5,8,9,11,12,22,32]. Conventionally, this task can be carried out using low pass filters [22]; however, these not only reduce noise but also blur edges.…”