Adaptive fuzzy segmentation of magnetic resonance images
Adaptive fuzzy segmentation of magnetic resonance images
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DOI：
10.1109/42.802752
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发表时间：
1999-09-01
影响因子：
10.6
通讯作者：
Prince, JL
中科院分区：
文献类型：
--
作者：
Pham, DL;Prince, JL
提出了一种模糊分割二维(2-D)和三维(3-D)多光谱磁共振(MR)图像的算法，该算法受到强度不均匀(也称为阴影伪影)的影响。该算法是作者在前人工作中提出的二维自适应模糊C均值算法(2-D AFCM)的扩展，该算法将图像的亮度不均匀建模为一个增益场，使图像的亮度在图像空间中平滑而缓慢地变化。它迭代地适应强度不均匀，并且是完全自动化的。在本文中，我们将二维AFCM完全推广到三维多光谱图像。考虑到三维图像数据的潜在规模，我们还描述了一种新的基于多重网格的快速算法来实现。我们使用模拟的MR数据显示，在分割受损图像时，3-D AFCM比标准的模糊C-均值(FCM)算法和其他两种竞争的方法产生更低的错误率。使用真实的3-D标量和多光谱磁共振脑图像进一步证明了它的有效性。
An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors, This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.