A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
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DOI：
10.1016/j.artmed.2004.01.012
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发表时间：
2004-09-01
影响因子：
7.5
通讯作者：
Chen, SC
中科院分区：
文献类型：
--
作者：
Zhang, DQ;Chen, SC
图像分割在许多医学成像应用中起着至关重要的作用。本文提出了一种新的磁共振成像（MRI）数据模糊分割算法。该算法利用核诱导距离度量和隶属函数的空间惩罚对传统模糊c均值（FCM）算法中的目标函数进行修正。首先，将FCM中原有的欧氏距离替换为核诱导距离，从而推导出相应的算法，称为核化模糊c均值（KFCM）算法，该算法比FCM具有更强的鲁棒性。然后在KFCM的目标函数中加入空间惩罚，以补偿MR图像的强度不均匀性，并允许图像中相邻像素的标记受到其影响。惩罚项充当正则化器，其系数范围从0到1。在合成和真实MR图像上的实验结果表明，在存在噪声和其他伪影的情况下，该算法比标准算法具有更好的性能。(C) 2004 Elsevier B.V.版权所有
Image segmentation plays a crucial role in many medical imaging applications. In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data. The algorithm is realized by modifying the objective function in the conventional fuzzy C-means (FCM) algorithm using a kernel-induced distance metric and a spatial penalty on the membership functions. Firstly, the original Euclidean distance in the FCM is replaced by a kernel-induced distance, and thus the corresponding algorithm is derived and called as the kernelized fuzzy C-means (KFCM) algorithm, which is shown to be more robust than FCM. Then a spatial penalty is added to the objective function in KFCM to compensate for the intensity inhomogeneities of MR image and to allow the labeling of a pixel to be influenced by its neighbors in the image. The penalty term acts as a regularizer and has a coefficient ranging from zero to one. Experimental results on both synthetic and real MR images show that the proposed algorithms have better performance when noise and other artifacts are present than the standard algorithms. (C) 2004 Elsevier B.V. All rights reserved.