针对焦炭显微图像中光学组织的特点，提出了一种结合均值偏移和边缘置信度的焦炭显微图像分割。该方法首先计算图像像素的边缘置信度，利用边缘置信度设计均值偏移算法中的权值函数，对特征空间的采样点进行加权，以提高模式点检测的准确性；然后以扩展的均值偏移向量进行迭代，实现焦炭显微图像的初步分割；由于在初步分割中产生过多的聚类数，导致了相同组分区域的过分割。因此通过空域距离和区域边界像素的置信度平均值设置合并条件，合并相同光学组分的焦炭区域，实现图像的最终分割。实验表明，该方法能够有效地分割出焦炭显微图像中不同光学组织组分区域，为焦炭光学组织的自动识别提供可靠依据。

In view of characteristics for coke optical texture in micrograph, a segmentation algorithm，combining mean shift and edge confidence, is proposed. Firstly, the edge confidence of image pixels is calculated. With the edge confidence, the weighting function of mean shift algorithm is computed. The sampling points of feature space are weighted in order to improve the accuracy of detected modes. Secondly, coke optical texture is segmented preliminarily by iterating the weighted mean shift vector. Because that the number of clusters in initial segmentation is larger than that of the actual clusters, which may result in over-segmentation, combining conditions are set by the spatial distance and the average value of the edge confidence, which are used to combine regions of homogenous texture. The coke optical texture is finally segmented with the new combining conditions. Experimental results show that with the proposed algorithm the segmentation among different optical textures of coke is reasonable and effective, which offers a reliable foundation for the recognition of coke optical texture.