基于对称信息的改进模糊c均值算法在脑磁共振图像分割中的应用

S. A. Jayasuriya, Alan Wee-Chung Liew
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引用次数: 6

摘要

本文提出了一种基于对称信息的改进模糊c均值算法,以减少磁共振图像中脑组织分割中噪声的影响。我们将大脑的双侧对称性作为一个附加项整合到传统的模糊c均值(FCM)中。在实验中,使用了一些合成图像以及模拟和真实的脑图像来研究该方法对噪声的鲁棒性。最后,将该方法与传统FCM算法进行了比较。结果表明,该方法是可行的,初步的研究是有希望的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A modified Fuzzy C-means algorithm with symmetry information for MR brain image segmentation
In this paper, we present a novel modified Fuzzy C-means algorithm with symmetry information to reduce the effect of noise in brain tissue segmentation in magnetic resonance image (MRI). We integrate brain's bilateral symmetry into the conventional Fuzzy C-means (FCM) as an additional term. In experiments, some synthetic images, and both simulated and real brain images were used to investigate the robustness of the method against noise. Finally, the method was compared with the conventional FCM algorithm. Results show the viability of the approach and the preliminary investigation appears promising.
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