Department of Mathematics, Pondicherry Central University, India.
Comput Biol Med. 2013 Feb;43(2):73-83. doi: 10.1016/j.compbiomed.2012.10.002. Epub 2012 Dec 6.
The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.
本文的主要动机是引入一类用于原始数据空间的鲁棒非欧几里得距离度量，以导出新的目标函数，从而对数据中的非欧几里得结构进行聚类，增强原始聚类算法对噪声和离群点的鲁棒性。所提出算法的新目标函数通过将噪声聚类概念合并到基于熵的模糊 C 均值算法中，并使用合适的噪声距离来实现，该距离用于在聚类过程中获取有关噪声数据的信息。本文使用原型初始化方法提出初始聚类原型，以使这项工作尝试通过较少的迭代次数获得最终结果。为了评估所提出方法在降低噪声水平方面的性能，使用被高斯噪声污染的合成图像进行了实验工作。通过对医学图像的实验研究，检验了所提出方法的优越性。实验结果表明，所提出的算法比标准的现有算法表现要好得多。使用轮廓有效性指数获得了所提出的模糊 C 均值分割方法的准确分类百分比。
