基于小波变换的医学图像噪声滤除方法的研究

目的：研究一种基于小渡变换的医学图像噪声滤除方法，并比较不同小波函数的去噪效果。方法：提出了一种利用小渡局部系数改进的软阂值方法。首先,应用小波变换得到图像的局部模极值分布Mj,m,n^ψ然后．计算小渡变换的M模极大值，根据局部模极值分布的统计特性来设定一个阈值门限Tm：当小渡变换的模极值大于等于阈值门限Tm时，其对应的小波系数保持不变：当小渡变换的模极值小于阈值门限Tm时，其对应的小渡系数通过软闽值法进行计算。最后,根据这两部分的小渡系数进行小波逆变换重构图像。结果：所提出的方法能有效地滤除医学图像中的噪声，不同小波的噪声滤除效果有一定的差异。结论：选择合适的小渡基函数来对图像进行小渡多尺度分解．可以得到比较完善的小渡阈值去噪算法．达到比较理想的去噪效果．

Investigation of a Wavelet Transform Based Noise Filtering Approach for Medical Images

Objective To investigate a wavelet-transform-based approach that reduces the noise of medical images, and to compare the difference of the effects by different wavelet types. Methods A soft threshold approach based on the modification of local coefficient of wavelets was proposed. Firstly, a local modulus extrema distribution of the image, M j,m,n is obtained using wavelet transform. Then the modulus maximum was calculated and a threshold Tm was defined according to the statistical properties of the local modulus extrema distribution. If the extremum of the wavelet transform was greater than or equal to the threshold Tm, the corresponding wavelet coefficient was kept unchanged; while if the extremum of the wavelet transform was less than the threshold Tm, its corresponding wavelet coefficient was calculated using the soft threshold approach. Lastly, an inverse wavelet transform was performed according to the wavelet coefficients of these two parts so that the image could be reconstructed. Results The proposed approach could filter out the noise in medical images effectively, and the effects of noise reduction by different wavelets were different. Conclusion A useful wavelet threshold noise reduction algorithm can be obtained by wavelet multi-dimensional decomposition of image with proper selection of wavelet base function, and comparatively ideal effect of noise reduction can be achieved using this algorithm.