摘要
针对核磁共振医学图像含有的混合噪声的特点,提出了一种基于2维经验模式分解(BEMD)和小波阈值去噪的新算法,即将图像分解到固有模态函数(IMF)域。然后采用小波阈值法对各固有模态函数成分进行去噪处理。在分析了小波硬阈值和软阈值去噪的特点之后,对小波阈值进行了改进,克服了传统小波阈值去噪的不足。实验结果表明该方法在有效去除噪声的同时,较好地保留了MRI图像的细节,有利于医学的诊断。
A new method which is based on bidimensional empirical mode decomposition （BEMD） and wavelet thresholding was proposed for the noise removal in medical image of magnetic resonance image（MRI）. Namely the image was decomposed into the intrinsic mode function （IMF） domain. Then the wavelet thresholding was used to remove the noise in the IMF. After the characteristic of the wavelet hard thresholding and the wavelet soft thresholding was analyzed, an improved wavelet thresholding which overcomes the shortcoming of the custom wavelet thresholding for denoising was introduced. In addition to remove the noise in MRI image, the experimental results show that our method had preserved the details of MRI image. It was propitious to medical diagnoses.
出处
《中国图象图形学报》
CSCD
北大核心
2009年第10期1972-1977,共6页
Journal of Image and Graphics
基金
湖南省自然科学基金项目(07JJ3120
09JJ3120)
湖南省高等学校科学研究重点项目(08A001)
关键词
核磁共振图像
2维经验模式分解
固有模态函数
小波阈值去噪
magnetic resonance image, bidimensional empirical mode decomposition, intrinsic mode function, wavelet thresholding denoising
