An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation
An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation
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DOI：
10.1109/tmi.2003.816956
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发表时间：
2003-09-01
影响因子：
10.6
通讯作者：
Yan, H
中科院分区：
文献类型：
--
作者：
Liew, AWC;Yan, H
提出了一种自适应空间模糊c-均值聚类算法用于三维磁共振图像的分割。输入图像可能被噪声和强度不均匀性（INU）伪影破坏。该算法考虑到空间连续性的约束，通过使用相异性指数，允许图像体素之间的空间相互作用。局部空间连续性约束减少了噪声影响和分类模糊性。INU伪影被公式化为影响真实MR成像信号的乘性偏置场。通过将对数偏置场建模为平滑B样条表面的堆叠，在切片上强制连续性，3-D偏置场的计算减少到找到B样条系数的计算，B样条系数可以使用计算高效的两阶段算法获得。所提出的算法的有效性证明了广泛的分割实验，使用模拟和真实的MR图像，并与其他已发表的算法进行比较。
An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.