基于多目标规划的模糊C均值聚类算法

模糊C均值聚类算法(FCM)是一种非常经典的非监督聚类技术,已被广泛地应用到医学图像分割。由于传统的FCM聚类算法在分割图像时仅利用了图像的灰度信息,未利用图像的空间信息,在分割叠加了噪声的磁共振(MR)图像时分割效果不理想。考虑到脑部MR图像真实的灰度值具有分片为常数的特性,按照合理利用图像空间信息的原则,对传统的FCM聚类算法进行了改进,引入多目标规划的概念,提出了一种新的,更加合理的应用图像空间信息的聚类算法。实验结果表明,应用该算法可以有效地分割含有噪声的图像。

An Improved FCM Algorithm Based on Multiple Objective Programming

Fuzzy C means(FCM) clustering algorithm is one of well known unsupervised clustering techniques, which has been widely used in medical image segmentation. However, when the conventional FCM algorithm is used for image segmentation, no spatial information is taken into account. This causes the FCM algorithm to generate unexpected results of segmentation when dealing with noise of magnetic resonance images(MRI). Considering the intensities of ideal MRI are piecewise constant, we present an improved model to FCM algorithms using multiple objective programming. The algorithm can reasonably use the spatial information of the image and improve the accuracy of segmentation. The new algorithm is applied to both synthetic images and magnetic resonance images and the results show better robustness of our algorithm to noise and other artifacts than the conventional fuzzy image segmentation algorithms.