Comput Math Methods Med - Nonlocal means-based denoising for medical images.

Tópicos

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Resumo

Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed.

Resumo Limpo

medic imag often consist lowcontrast object corrupt random nois aris imag acquisit process thus imag denois one fundament task requir medic imag analysi nonloc mean nlmean method provid power framework denois work investig adapt denois scheme base patch nlmean algorithm medic imag denois contrast tradit nlmean algorithm propos adapt nlmean denois scheme three uniqu featur first use restrict local neighbourhood true intens noisi pixel estim set select neighbour pixel perform denois process second weight use calcul thank similar patch denois patch candid final appli steer kernel preserv detail imag propos method compar similar stateofart method synthet real clinic medic imag show improv perform case analyz

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