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机构地区:[1]成都电子机械高等专科学校通信系,四川成都610031 [2]四川大学,四川成都610064
出 处:《通信技术》2007年第11期379-381,共3页Communications Technology
摘 要:磁共振成像已成为脑功能病理和解剖研究的主要手段,是医学影像学领域中最活跃的技术。由于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,使得磁共振图像去噪成为很重要的研究热点。小波分析具有多尺度分辨和去相关性等特点,在去除被白噪声污染的磁共振图像方面得到了广泛应用。但磁共振图像经传统的小波分析去噪后,细节信息部分丢失,图像的边缘变得模糊。针对这些问题,对经典的小波阀值去噪方法进行了改进,将关键参数取值与预估计联系起来,将阀值的选定与图像的局部特征结合起来,提出一种灵活的、自适应的去噪新方法。与经典方法相比,采用本方法处理的噪声图像去噪后图像的细节更丰富,边缘信息完善,视觉效果更好。Currently, magnetic resonance imaging technology has become an important method in brain function research field, and it is the most active technology in medical image. To filter the interference brought by body thermal noise during imaging procedure, it is necessary to research the de-noise method. With multi-scales and de-relevence, wavelet analysis is better than typical methods in denoising magnetic resonance image polluted by Gauss noise. So, wavelet analysis denoising methods are applied widely in magnetic resonance image polluted by Gauss noise. However, with wavelet analysis, detail information of magnetic resonance images are weakened, edges of images are blurred. To solve these problems, a new magnetic resonance image denoising method of threshold based on wavelet analysis is proposed. This is a modified method based on typical wavelet analysis. It combines key parameter with pre-estimation, and makes the threshold more measurable than that in typical method and has self-compatibility. Compared with the classical wavelet denoising method, this method makes edges more clear, enhances texture characters and improves the performance of denoising.
分 类 号:TP751[自动化与计算机技术—检测技术与自动化装置]
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