Comput Math Methods Med - GPU-based block-wise nonlocal means denoising for 3D ultrasound images.

Tópicos

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Resumo

Speckle suppression plays an important role in improving ultrasound (US) image quality. While lots of algorithms have been proposed for 2D US image denoising with remarkable filtering quality, there is relatively less work done on 3D ultrasound speckle suppression, where the whole volume data rather than just one frame needs to be considered. Then, the most crucial problem with 3D US denoising is that the computational complexity increases tremendously. The nonlocal means (NLM) provides an effective method for speckle suppression in US images. In this paper, a programmable graphic-processor-unit- (GPU-) based fast NLM filter is proposed for 3D ultrasound speckle reduction. A Gamma distribution noise model, which is able to reliably capture image statistics for Log-compressed ultrasound images, was used for the 3D block-wise NLM filter on basis of Bayesian framework. The most significant aspect of our method was the adopting of powerful data-parallel computing capability of GPU to improve the overall efficiency. Experimental results demonstrate that the proposed method can enormously accelerate the algorithm.

Resumo Limpo

speckl suppress play import role improv ultrasound us imag qualiti lot algorithm propos d us imag denois remark filter qualiti relat less work done d ultrasound speckl suppress whole volum data rather just one frame need consid crucial problem d us denois comput complex increas tremend nonloc mean nlm provid effect method speckl suppress us imag paper programm graphicprocessorunit gpu base fast nlm filter propos d ultrasound speckl reduct gamma distribut nois model abl reliabl captur imag statist logcompress ultrasound imag use d blockwis nlm filter basi bayesian framework signific aspect method adopt power dataparallel comput capabl gpu improv overal effici experiment result demonstr propos method can enorm acceler algorithm

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