改进的基于核函数的FCM图像分割算法

A Modified Kernel-based Fuzzy C-Means Algorithm For Images Segmentation

目的:对含有噪声的脑部MR图像进行分割。方法:改进了传统FCM目标函数,引入核函数的概念,用内核诱导距离来代替传统的欧氏距离;考虑到邻近像素的影响,增加了空间约束项,从而提出了一种能够合理应用图像空间信息的、改进的基于核函数的模糊C均值聚类算法。结果:对叠加椒盐噪声模拟图分割,该算法分割结果显示各灰度区域内无噪声污点;对仿真脑部MR图像分割,该算法错分率低于基于核函数的FCM算法其及改进算法。结论:与传统的FCM和其他改进算法相比,该算法能够对非精确图像进行更精确的分割。

Objective To segment brain magnetic resonance （MR） images corrupted by noises. Mothods We presented a novel Fuzzy C-Means （FCM） algorithm for image segmentation. The algorithm was by modifying the objective function in the conventional FCM. Firstly, by using kernel method, the original Euclidean distance in the FCM was replaced by a kernel-induced distance. Then, a spatial penalty term was added to the objective function to compensate the influence of the neighboring pixels on the center pixel. Results Segmentation results on a four-class synthetic image corrupted by salt ＆ pepper noise shows that the new algorithm is less speckled and smoother. The new algorithm is applied to simulation MR images and is shown to have less misclassification rate than the other FCM-based methods. Conclusion The results of experiments show that the proposed algorithm is more robust to noise than other FCM-based methods.[Chinese Medical Equipment Journal, 2009,30 （9） ： 31-33]