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- W806098558 abstract "AbstractThis paper proposes an improved spatial fuzzy segmentation of a noisy image, based on a hybrid of stationary wavelet thresholding and filtering preprocess. The proposed methods aim to improve the segmentation by reducing the effect of additive noise during preprocess. Noise filtering as well as wavelet thresholding are carried out in each stationary wavelet subbands. Thus, noise distributed across any subband coefficients can be examined. This would lead to image denoising improvement. Afterwards, fuzzy c-means incorporated with spatial information (sFCM) is utilized for segmenting the denoised image. The denoising preprocess and segmentation measurements rely on peak signal-to-noise ratio (PSNR) and Xie-Beni (XB) validity index respectively. T1-weighted MRI is tested with salt-and-pepper and Gaussian additive noise. Based on experimental results, the proposed hybrid methods improve the segmentation more efficiently than comparative traditional denoising methods.KeywordsStationary wavelet transformNoise filteringT1-weighted MRIFuzzy c-means" @default.
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- W806098558 date "2015-01-01" @default.
- W806098558 modified "2023-09-27" @default.
- W806098558 title "Improving Noisy T1-Weighted MRI Spatial Fuzzy Segmentation Based on a Hybrid of Stationary Wavelet Thresholding and Filtering Preprocess" @default.
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- W806098558 doi "https://doi.org/10.1007/978-3-319-19066-2_57" @default.
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