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- W2901474597 abstract "For proper modelling of signal and noise in MR data requires proper interpretation and analysis of data, the different approaches with this degradation due to random fluctuations in the MR data, probabilistic modeling is power solution, which needs correctness in the computation of noise is challenging task and various stastical approaches can be utilized. After modelling the noise it can be integrated to denoising pipeline, in this research work, the recognition of noise only pixels and the evaluation of standard deviation of noise using median, mean or other optimal sample quantiles are combined in to single frame work for noise assement and uses fixed point iterative procedure to obtain standard deviation of noise. We tested the effectiveness of the algorithm to the MR clinical and synthetic data base." @default.
- W2901474597 created "2018-11-29" @default.
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- W2901474597 date "2018-11-22" @default.
- W2901474597 modified "2023-09-24" @default.
- W2901474597 title "Probabilistic Identification and Estimation of Noise: Application to MR Images" @default.
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- W2901474597 doi "https://doi.org/10.13005/bpj/1589" @default.
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