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超声图像去噪是医学图像处理的研究热点之一 ,基于小波域阈值去噪技术及阈值选取方法的分析 ,提出一种新的医学超声图像小波域阈值去噪方法。这种方法采用半_软阈值去噪技术和广义交叉确认函数寻找阈值 ,在有效去噪的同时较好地保留了图像边缘细节。首先 ,把对数超声图像小波分解 ;然后 ,基于广义交叉确认函数寻找最小均方误差意义上的近似最优阈值 ,对所有的高频段采用半_软阈值去噪 ;最后 ,经小波反变换和指数变换获得去噪后的超声图像 ,文末对超声图像小波域阈值去噪方法作出定性比较 ,并对算法的去噪性能给出定量分析。仿真实验和实际测试结果表明此方法是有效的、可行的。
Ultrasound image de-noising is one of the hot spots in medical image processing. Based on the analysis of wavelet domain threshold de-noising technique and threshold selection method, a new threshold method of wavelet denoising in medical ultrasound image is proposed. This method uses the half-soft threshold denoising technique and the generalized cross-validation function to find the threshold, and effectively preserves the image edge details while effectively denoising. Firstly, wavelet decomposition is performed on the logarithmic ultrasound image. Then, based on the generalized cross-validation function, the approximate optimal threshold in the sense of minimum mean square error is found, and the half-soft threshold is applied to all the high frequency bands. Finally, Exponential transform to obtain the denoised ultrasound images, the text at the end of the ultrasound image wavelet threshold denoising methods to make a qualitative comparison, and the denoising performance of the algorithm given quantitative analysis. Simulation results and actual test results show that this method is effective and feasible.