一种C-均值聚类图像分割的模糊熵后处理方法

Fuzzy Entropy Based Post-Processing Method for C-Mean Clustering Image Segmentation Algorithm

提出了一种结合C-均值聚类算法和模糊熵的图像分割方法，该方法先采用C均值聚类算法对含噪图像进行初步分割，再利用模糊熵准则作后续处理。该方法一方面能够继承C-均值聚类算法的优点，可以灵活地用在基于多特征和多阂值的图像分割中，另一方面充分考虑了图像的区域信息，利用模糊熵最小作为准则，对c均值聚类算法初步分割结果的错分类点作了进一步的处理，克服了C-均值聚类算法对噪声敏感的缺点。实验结果表明，本文方法在运算开销上只比C-均值聚类算法多4～6S，对于低信噪比的图像能够取得优于C-均值聚类算法的分割效果。

An image segmentation method for combining C mean clustering algorithms and fuzzy entropy is presented. Firstly, the pre-segmentation on the image is made by one of the C- mean elustering algorithms; then further proeessing is clone by using fuzzy entropy principle. The method inherits the advantages of the C-mean elustering algorithms, that is, it ean be eas fly applied to image segmentation tasks with multi-feature and multi-threshold. Furthermore, the method considers the region information of the image, and utilizes the minimum fuzzy entropy prineiple to post-proeess the wrong classified points of the pre-segmentation result. Thus the method ean overeome the disadvantage of the C-mean elustering algorithms, that is, it is not sensitive to the noise. Experimental results show that the CPU-time of the method is only 4- 6 s more than the C-mean elustering algorithms, and it ean behave better in segmenting images of low signal to noise ratio than the C mean clustering algorithms.