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中文摘要：
医学图像（CT、MRI）的滤波处理，须保留具有重要诊断意义的边缘细节信息。针对Perona—Malik各向异性扩散模型病态且不稳定的不足，提出了一种改进的各向异性扩散滤波算法。通过采用自适应加权的多尺度形态滤波来改进扩散系数，建立了一个对噪声图像更有效和更具适应性的去噪扩散模型。同时引入迭代终止准则，避免了迭代次数的设定。实验结果表明，算法优于PM方法和Catte方法，在提高信噪比的同时又可保留重要的微细结构，可以较好地满足医学图像的使用要求。
英文摘要：
When medical images are filtered,it is necessary to preserve the important edges and key details.An improved anisotropic diffusion filtering algorithm is proposed according to the disadvantages of Perona-Malik model.The novel diffusion model is established based on morphological diffusion coefficient,which adopts multi-scale morphological filter with auto-adapted determinations weights.The improved scheme has superiority capability over the Perona-Malik scheme.Also an iteration stopping criterion is adopted to avoid computing the time.It has been shown from the experiments that this method can improve SNR,and at the same time it can retain important details structure.
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