Department of Electrical Engineering, National Cheng Kung University, Tainan 70701, Taiwan.
Comput Biol Med. 2010 Jun;40(6):572-9. doi: 10.1016/j.compbiomed.2010.04.001. Epub 2010 May 4.
The objective of this paper is to develop an effective robust fuzzy c-means for a segmentation of breast and brain magnetic resonance images. The widely used conventional fuzzy c-means for medical image segmentations has limitations because of its squared-norm distance measure to measure the similarity between centers and data objects of medical images which are corrupted by heavy noise, outliers, and other imaging artifacts. To overcome the limitations this paper develops a novel objective function based standard objective function of fuzzy c-means that incorporates the robust kernel-induced distance for clustering the corrupted dataset of breast and brain medical images. By minimizing the novel objective function this paper obtains effective equation for optimal cluster centers and equation to achieve optimal membership grades for partitioning the given dataset. In order to solve the problems of clustering performance affected by initial centers of clusters, this paper introduces a specialized center initialization method for executing the proposed algorithm in segmenting medical images. Experiments are performed with synthetic, real breast and brain images to assess the performance of the proposed method. Further the validity of clustering results is obtained using silhouette method and this paper compares the results with the results of other recent reported fuzzy c-means methods. The experimental results show the superiority of the proposed clustering results.
本文旨在开发一种有效的稳健模糊 C 均值，用于分割乳腺和脑部磁共振图像。由于其平方范数距离度量，广泛用于医学图像分割的传统模糊 C 均值在测量医学图像中心和数据对象之间的相似度方面存在局限性，而这些医学图像受到严重噪声、异常值和其他成像伪影的干扰。为了克服这些限制，本文在模糊 C 均值的标准目标函数的基础上，开发了一个新的目标函数，该函数结合了稳健的核诱导距离，用于对乳腺和脑部医学图像的污染数据集进行聚类。通过最小化新的目标函数，本文获得了用于最佳聚类中心的有效方程，以及用于对给定数据集进行最佳划分的最佳隶属度等级的方程。为了解决聚类性能受聚类中心初始值影响的问题，本文引入了一种专门的中心初始化方法，用于执行用于分割医学图像的提议算法。本文使用合成、真实的乳腺和脑部图像进行实验，以评估所提出方法的性能。进一步，使用轮廓方法获得聚类结果的有效性，并将结果与其他最近报道的模糊 C 均值方法的结果进行比较。实验结果表明了所提出的聚类结果的优越性。
