Ahmed Mohamed N, Yamany Sameh M, Mohamed Nevin, Farag Aly A, Moriarty Thomas
Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt.
IEEE Trans Med Imaging. 2002 Mar;21(3):193-9. doi: 10.1109/42.996338.
In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
在本文中，我们提出了一种用于磁共振成像（MRI）数据模糊分割及利用模糊逻辑估计强度不均匀性的新算法。MRI强度不均匀性可能归因于射频线圈的缺陷或与采集序列相关的问题。其结果是图像上出现缓慢变化的阴影伪影，这可能会给传统的基于强度的分类带来误差。我们的算法是通过修改标准模糊c均值（FCM）算法的目标函数来制定的，以补偿此类不均匀性，并允许像素（体素）的标记受到其紧邻邻域中标记的影响。邻域效应起到正则化作用，并使解决方案偏向于分段均匀标记。这种正则化在分割受椒盐噪声干扰的扫描图像时很有用。给出了在合成图像和MR数据上的实验结果，以证明所提算法的有效性和效率。
