A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation
A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation
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DOI：
10.1109/tsmcb.2011.2124455
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发表时间：
2011-10-01
影响因子：
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通讯作者：
Lu, Mingzhu
中科院分区：
文献类型：
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作者：
Chen, Long;Chen, C. L. Philip;Lu, Mingzhu
本文提出了一种广义多核模糊C均值(MKFCM)方法作为图像分割问题的框架。在该框架中，除了在核FCM(KFCM)中使用复合核之外，还提出了多个核的线性组合，并推导了复合核线性系数的更新规则。提出的MKFCM算法为我们在图像分割问题中融合不同像素信息提供了一种新的灵活的工具。也就是说，由不同核表示的不同像素信息在核空间中被组合以产生新的核。结果表明，两种成功的基于增强KFCM的图像分割算法都是MKFCM的特例。从提出的MKFCM框架中还得到了几种新的分割算法。通过对合成图像和医学图像的分割实验，验证了基于MKFCM的分割方法的灵活性和优越性。
In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed and the updating rules for the linear coefficients of the composite kernel are derived as well. The proposed MKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in image-segmentation problems. That is, different pixel information represented by different kernels is combined in the kernel space to produce a new kernel. It is shown that two successful enhanced KFCM-based image-segmentation algorithms are special cases of MKFCM. Several new segmentation algorithms are also derived from the proposed MKFCM framework. Simulations on the segmentation of synthetic and medical images demonstrate the flexibility and advantages of MKFCM-based approaches.