C均值和模糊C均值聚类是两种非常流行的图像分割算法。
C-means and fuzzy c-means clustering are two very popular image segmentation algorithms.
虽然它们的实现是直观的，但如果只是简单地实现，将导致执行时间和内存消耗的巨大开销。
While their implementation is straightforward, if realized naively it will lead to substantial overhead in execution time and memory consumption.
尽管对于小型的二维图像，这些缺陷可以忽略，但这些缺点对于大的三维数据集更为明显。
Although these deficiencies could be ignored for small 2D images they become more noticeable for large 3D datasets.
本文旨在为N维灰度图像分割提供这些算法的有效实现。
This submission is intended to provide an efficient implementation of these algorithms for segmenting N-dimensional grayscale images.
利用聚类过程中图像强度的直方图代替原始图像数据，从而提高了计算效率。
The computational efficiency is achieved by using the histogram of the image intensities during the clustering process instead of the raw image data.
最后，由于算法是从零开始实现的，因此不依赖于任何辅助工具箱。
Finally, since the algorithms are implemented from scratch there are no dependencies on any auxiliary toolboxes.
要快速演示如何使用该函数，请运行“DemoFCM”文件。
For a quick demonstration of how to use the functions, run the attached ‘DemoFCM’ file.
完整源码下载地址：
http://page2.dfpan.com/fs/7lec6j22e2714249163/
更多精彩文章请关注微信号：