Diaz Idanis, Boulanger Pierre, Greiner Russell, Murtha Albert
Dept of Comput Science, U of Alberta, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:3934-7. doi: 10.1109/IEMBS.2011.6090977.
One can find in the literature numerous techniques to reduce noise in Magnetic Resonance Images (MRI). This paper critically reviews modern de-noising algorithms (Gaussian filter, anisotropic diffusion, wavelet, and non-local mean) in terms of their efficiency, statistical assumptions, and their ability to improve brain tumor segmentation results. We will show that although different techniques do reduce the noise, many generate artifacts that are incompatible with precise brain tumor segmentation. We also show that the non-local means algorithm is the best de-noising technique for brain tumor segmentation.
人们可以在文献中找到许多减少磁共振成像（MRI）噪声的技术。本文从效率、统计假设以及改善脑肿瘤分割结果的能力等方面，对现代去噪算法（高斯滤波器、各向异性扩散、小波和非局部均值）进行了批判性综述。我们将表明，尽管不同的技术确实能降低噪声，但许多技术会产生与精确脑肿瘤分割不兼容的伪影。我们还表明，非局部均值算法是用于脑肿瘤分割的最佳去噪技术。
