Chen Long, Chen C L Philip, Lu Mingzhu
Department of Electrical and Computer Engineering, The University of Texas, San Antonio, TX 78249-0669, USA.
IEEE Trans Syst Man Cybern B Cybern. 2011 Oct;41(5):1263-74. doi: 10.1109/TSMCB.2011.2124455. Epub 2011 Apr 5.
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.
本文介绍了一种广义多内核模糊C均值（FCM）（MKFCM）方法，作为图像分割问题的一个框架。在该框架中，除了在内核FCM（KFCM）中使用复合内核这一事实外，还提出了多个内核的线性组合，并推导了复合内核线性系数的更新规则。所提出的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手段。也就是说，由不同内核表示的不同像素信息在内核空间中进行组合以产生一个新的内核。结果表明，两种成功的基于增强KFCM的图像分割算法是MKFCM的特殊情况。还从所提出的MKFCM框架中推导出了几种新的分割算法。对合成图像和医学图像进行分割的仿真证明了基于MKFCM方法的灵活性和优势。
