Automatic Bilateral Filtering on Brain Magnetic Resonance Images Using a Back-Propagation Neural Network
碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 101 === The bilateral filter has been widely used in many image processing applications. It is an effective filtering algorithm that can remove the random noise in magnetic resonance (MR) images. However, the bilateral filter requires the end-user to try different...
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| Format: | Others |
| Language: | zh-TW |
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2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/52820272011704102839 |
| Summary: | 碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 101 === The bilateral filter has been widely used in many image processing applications. It is an effective filtering algorithm that can remove the random noise in magnetic resonance (MR) images. However, the bilateral filter requires the end-user to try different combinations of parameter values in order to obtain the optimal filtering results. This testing process is very time-consuming and difficult to know whether the reconstructed images have the optimal quality or not. To solve this problem, this thesis proposes using the MR image features in combined with a back propagation neural network to establish a predictable parameter model. The goal is to use this model to optimize parameters settings and to automate the denoising procedure. We adopt the gray level co-occurrence matrix and the discrete wavelet transform method for image features extraction. The T-test method is then used to select the features that can effectively distinguish characteristics differences in image data. We have used a wide variety of simulated T1-weighted MR images to evaluate the proposed automatic denoising system. The experimental results indicated that the proposed method effectively predicted the bilateral filtering parameters and automatically removed the noise in MR images. In summary, this new method creates a prediction model with high predictive accuracy and produces reconstructed images with good quality both qualitatively and quantitatively.
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