Comput Math Methods Med - Improving spatial adaptivity of nonlocal means in low-dosed CT imaging using pointwise fractal dimension.

Tópicos

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Resumo

NLMs is a state-of-art image denoising method; however, it sometimes oversmoothes anatomical features in low-dose CT (LDCT) imaging. In this paper, we propose a simple way to improve the spatial adaptivity (SA) of NLMs using pointwise fractal dimension (PWFD). Unlike existing fractal image dimensions that are computed on the whole images or blocks of images, the new PWFD, named pointwise box-counting dimension (PWBCD), is computed for each image pixel. PWBCD uses a fixed size local window centered at the considered image pixel to fit the different local structures of images. Then based on PWBCD, a new method that uses PWBCD to improve SA of NLMs directly is proposed. That is, PWBCD is combined with the weight of the difference between local comparison windows for NLMs. Smoothing results for test images and real sinograms show that PWBCD-NLMs with well-chosen parameters can preserve anatomical features better while suppressing the noises efficiently. In addition, PWBCD-NLMs also has better performance both in visual quality and peak signal to noise ratio (PSNR) than NLMs in LDCT imaging.

Resumo Limpo

nlms stateofart imag denois method howev sometim oversmooth anatom featur lowdos ct ldct imag paper propos simpl way improv spatial adapt sa nlms use pointwis fractal dimens pwfd unlik exist fractal imag dimens comput whole imag block imag new pwfd name pointwis boxcount dimens pwbcd comput imag pixel pwbcd use fix size local window center consid imag pixel fit differ local structur imag base pwbcd new method use pwbcd improv sa nlms direct propos pwbcd combin weight differ local comparison window nlms smooth result test imag real sinogram show pwbcdnlms wellchosen paramet can preserv anatom featur better suppress nois effici addit pwbcdnlms also better perform visual qualiti peak signal nois ratio psnr nlms ldct imag

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