The microstructure is the key factor for quality discriminate of coke. In view of the characteristics of coke optical tissue (COT), a segmentation method of coke microstructures based on adaptive clustering was proposed. According to the strategy of multiresolution, adaptive threshold binarization and morphological filtering were carried out on COT images with lower resolution. The contour of the COT body was detected through the relationship checking between contours in the binary image, and hence, COT pixels were picked out to cluster for tissue segmentation. In order to get the optimum segmentation for each tissue, an advanced -means method with adaptive clustering centers was provided according to the Calinski-Harabasz score. Meanwhile, Euclidean distance was substituted with Mahalanobis distance between each pixel in HSV space to improve the accuracy. The experimental results show that compared with the traditional -means algorithm, FCM algorithm, and Meanshift algorithm, the adaptive clustering algorithm proposed in this paper is more accurate in the segmentation of various tissue components in COT images, and the accuracy of tissue segmentation reaches 94.3500%.

中文翻译：

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基于自适应聚类的焦炭光学组织分割方法研究

微观结构是区分焦炭质量的关键因素。针对焦炭光学组织（COT）的特点，提出了一种基于自适应聚类的焦炭微观结构分割方法。根据多分辨率策略，对分辨率较低的COT图像进行自适应阈值二值化和形态滤波。通过二值图像中轮廓之间的关系检测来检测COT体的轮廓，从而选取COT像素进行聚类进行组织分割。为了获得每个组织的最佳分割，先进的-根据 Calinski-Harabasz 分数提供具有自适应聚类中心的平均方法。同时，将欧氏距离替换为 HSV 空间中每个像素之间的马氏距离以提高精度。实验结果表明，与传统的-均值算法、FCM算法和Meanshift算法相比，本文提出的自适应聚类算法在对COT图像中各种组织成分的分割上更加准确，组织分割精度达到94.3500 %。