Homomorphic wavelet thresholding technique for denoising medical ultrasound images

Abstract

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.

Improved illumination invariant homomorphic filtering using the dual tree complex wavelet transform

“…Spatial filters such as Difference of Gaussian (DoG) filters [8] and wavelets [2,3,[5][6][7] have been used to locally implement the high pass filter H.…”

“…al. [2] have just implemented hard and soft thresholding constraints within all of the high pass wavelet subbands. Conversely, Gorgel et.…”

“…Conventionally, Homomorphic filtering is implemented using an isotropic high pass filter (such as the Butterworth filter) within the Fourier domain [1]. However, recent work has used wavelet transforms [2,3,[5][6][7].…”

“…It is used in many imaging applications such as medical image enhancement [2][3][4], illumination invariant pre-processing for face recognition [5][6][7][8] and general image illumination normalisation [1,9]. Conventionally, Homomorphic filtering is implemented using an isotropic high pass filter (such as the Butterworth filter) within the Fourier domain [1].…”

Ultrasonic liver image denoising based on a hybrid threshold method

“…In the reported literatures, speckle reduction approaches for medical ultrasound image include spatial filtered method [1,3,5,8,9,11] and multiscale denoising methods [2,4,6,7,10,14,16].…”