一种粗糙熵和K-均值聚类相结合的岩心图像分割方法

K-均值聚类算法和粗糙熵是应用于图像分割的主要算法，目的是对图像进行分析处理。将K-均值聚类算法和粗糙熵结合起来应用到岩心图像的分割，目的是提取出岩石的隙缝信息。先利用K-均值聚类算法对岩心图像进行区域分割，再利用基于粗糙熵的方法对分割结果进行目标提取，从而达到多阚值分割的目的。通过效果图对比分析可以看出，采用基于粗糙熵的K-均值聚类算法处理多目标的岩心图像，提取出的目标更清晰，更明确，实验结果更有价值，证明了改进后算法的有效性。

Rock Image Segmentation Based on Rough Entropyand K-Means Clustering Algorithm

K-means clustering algorithm and rough entropy were mainly used to segment the image for analyzing the image.K-means clustering algorithm and rough entropy were combined to segment the image of rock,and gain the information of the rock rift.K-means clustering algorithm was used to segment the image then the algorithm based on rough entropy was used to extract objects from the segmentation results,so as to achieve multi-threshold segmentation.By contrasting the final result,it shows that the effect of using K-means clustering algorithm based on rough entropy to segment the multi-object rock image is better than other methods,and the result is clear and more valuable.It means that the ameliorated method is more valid.