Yu Xue-fei, Li Bin, Chen Wu-fan
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515ìChina.
Nan Fang Yi Ke Da Xue Xue Bao. 2008 Apr;28(4):555-7.
Fuzzy clustering technique is a popular model widely used in the segmentation of magnetic resonance (MR) images. However, when the conventional fuzzy clustering algorithm is used for image segmentation, the algorithm strictly depending on the current pixels works only on images with less noise. In the paper, we presented a modified fuzzy kernel clustering algorithm for MR image segmentation. The new algorithm incorporates a kernel-induced distance mertric and a penalty term that controls the neighborhood effect to the objective function. The results of experiment on both the synthetic images and simulated MR images show that the proposed algorithm is more robust to noise than the standard fuzzy image segmentation algorithms.
模糊聚类技术是一种广泛应用于磁共振（MR）图像分割的流行模型。然而，当使用传统的模糊聚类算法进行图像分割时，该算法严格依赖于当前像素，仅适用于噪声较少的图像。在本文中，我们提出了一种用于MR图像分割的改进模糊核聚类算法。新算法引入了核诱导距离度量和一个控制邻域对目标函数影响的惩罚项。在合成图像和模拟MR图像上的实验结果表明，与标准模糊图像分割算法相比，该算法对噪声更具鲁棒性。
