【摘要】 为了克服医学图像采集和传输过程中的图像模糊和边缘丢失现象,提出了一种基于小波变换和改进中值滤波的医学图像耦合去噪方法。该方法由四个模块组成:图像采集,图像存储,图像处理和图像重建。图像采集,获取包含高斯噪声和脉冲噪声的医学图像。图像存储,包括原始图像和处理后图像的数据、参数等信息的保存。在第三模块中,通过小波分解将医学图像分解为四个子带(LL,HL,LH,HH),其中LL为低频部分,LH,HL,HH分别为水平,垂直和对角线高频部分。利用改进的小波阈值处理高频系数,保留低频系数,并在小波阈值处理后对三个高频子带进行改进中值滤波处理。最后一个模块是图像重建,即通过小波重构去噪后的图像。这种方法的优点是结合中值滤波和小波的优点,去噪效果更好,而不是前两种方法的简单组合。通过DWT和改进的中值滤波器系数耦合去噪,对于包含复杂噪声的高精度医学图像处理非常实用。将所提算法的实验结果与中值滤波,小波变换,Contourlet和DT-CWT等方法的结果进行了比较。根据视觉评价指标PSNR和SNR以及Canny边缘检测得出:在低噪声图像中,PSNR和SNR分别提高10%～15%;在高噪声图像中,PSNR和SNR增加2%～6%。与其他方法相比,该算法取得了较好的结果,为医学诊断提供了重要方法。更多还原

【Abstract】 In order to overcome the phenomenon of image blur and edge loss in the process of collecting and transmitting medical image, a denoising method of medical image based on discrete wavelet transform(DWT) and modified median filter for medical image coupling denoising is proposed. The method is composed of four modules: image acquisition, image storage, image processing and image reconstruction. Image acquisition gets the medical image that contains Gaussian noise and impulse noise. Image storage includes the preservation of data and parameters of the original image and processed image. In the third module, the medical image is decomposed as four sub bands(LL, HL, LH, HH) by wavelet decomposition, where LL is low frequency, LH, HL, HH are respective for horizontal, vertical and in the diagonal line high frequency component. Using improved wavelet threshold to process high frequency coefficients and retain low frequency coefficients, the modified median filtering is performed on three high frequency sub bands after wavelet threshold processing. The last module is image reconstruction,which means getting the image after denoising by wavelet reconstruction. The advantage of this method is combining the advantages of median filter and wavelet to make the denoising effect better, not a simple combination of the two previous methods. With DWT and improved median filter coefficients coupling denoising, it is highly practical for high-precision medical images containing complex noises. The experimental results of proposed algorithm are compared with the results of median filter, wavelet transform, contourlet and DT-CWT, etc. According to visual evaluation index PSNR and SNR and Canny edge detection, in low noise images, PSNR and SNR increase by 10%–15%; in high noise images, PSNR and SNR increase by 2%–6%. The experimental results of the proposed algorithm achieved better acceptable results compared with other methods, which provides an important method for the diagnosis of medical condition.更多还原