导　　师： 陈武凡

学科专业： 0831

授予学位： 硕士

作　　者： ;

机构地区： 南方医科大学

摘　　要： 磁共振成像/(magnetic resonance imaging,MRI/)是与CT、同位素扫描、超声图像等一样重要的医学图像诊断手段。与其他医学成像方法相比,磁共振成像具有软组织对比度率高、对人体无电离辐射危害、能够反映器官或组织的生化特征等优点,已成为脑功能病理研究和心血管系统疾病诊断的主要手段,是医学影像学领域中最活跃的技术之一。但是,由于采集和变换过程复杂,医学磁共振图像在处理、传输过程中容易受到噪声污染,磁共振时间序列信号内的有用成分弱,而且在成像过程中复杂的电磁场环境容易受到人体热噪声干扰,造成噪声引起局部区域不清晰的现象,这给MRI图像的诊断分析和利用带来许多的困难。因此,探讨能消除MRI图像噪声的方法有着重要的临床意义和应用价值。 常用的磁共振图像去噪方法有中值滤波,维纳滤波,基于直方图的滤波等等。但是由于这些去噪方法对磁共振图像噪声成分分析得不够透彻,用它们来对磁共振图像去噪的时候,处理效果不能满足医学诊断的需求。近年来,小波分析和基于偏微分方程在图像去噪方面得到了广泛关注,但磁共振图像经过这些方法去噪后,边缘变得模糊。基于各向异性扩散滤波的图像平滑方法虽然在去除噪声的同时保留了图像边缘信息但是造成图像的细节信息部分丢失,容易造成临床医生的误诊或漏诊。 为了克服上述方法的不足,本文在基于梯度能量函数的全变分算法的基础之上,结合Bregman距离的迭代正则化方法,围绕全变分图像去噪的中心问题进行了研究,提出了相应的磁共振图像去噪算法。本文的方法综合了全变分和迭代正则化算法的优点,对迭代正则化算法中的全局正则化参数进行改进,给出了一种根据图像中不同区域的灰度分布特性,自适应选取正则化参数的方法。原来算法中的正则化参数对� Magnetic resonance imaging /(MRI/) diagnosis is an important medical image diagnosis means like CT, isotope scanning and ultrasound image to facilitate manual or computer-aids analysis. MRI is one of the most active technologies in medical imaging because of its high resolution, non-invasiveness. MRI, in particular, can produce images from human body non-invasively that reveal the structure, metabolism and function of internal organs or tissues. Currently, magnetic resonance imaging technology has become an important method in brain function and anatomy research field. Some important information of MRI images may be submerged due to the presence of noise. Therefore, to filter the interference brought by body thermal noise during imaging procedure, it is necessary to research the de-noise method. It is important for MRI images to be preprocessed before being used for medical analysis and application. Until now, a lot of common de-noise methods have been proposed, including median filter, wiener filter and histogram based filters. The quality of MRI images is improved to some extent, and to satisfy some determinate applications. However, these methods do not solve the defects of the MRI medical images radically. Presently wavelet analysis and the method based on PDE of image de-noising attract attention. But the two de-noising approaches have the tradeoff between de-noising and preserve the edge of the image. After processing edges of images are blurred. Image de-noising based on the anisotropic diffusion filtering can preserve the edge of the image. However, the text??characteristics of MRI images is weakened. As a result, it is difficult for doctor to diagnose different disease using blurred images. In order to solve these problems, a novel texture preserving variational de-noising method for MR images based on the use of adaptive regularization is proposed in this paper .This method combined the advantages of TV algorithm and iterative regularization algorithm based on Bregman distance. The new adapt

关 键 词： 磁共振 全变分 距离 图像去噪 自适应去噪

领　　域： [自动化与计算机技术] [自动化与计算机技术]