• Yuexing Han Shanghai University
  • Chuanbin Lai Shanghai University
  • Bing Wang Shanghai University
  • Tianyi Hu Shanghai University
  • Dongli Hu Shanghai University
  • Hui Gu Shanghai University



boundary detection, clustering algorithm, image segmentation, two-phase microstructure, virtual boundary


Microstructure of a material stores the genesis of the material and shows various properties of the material. To efficiently analyse the microstructure of a material, the segmentation of different phases or constituents is an important step. However, in general, due to the microstructure’s complexity, most of segmentation is manually done by human experts. It is challenging to automatically segment the material phases and the microstructure. In this work, we propose a method which combines the the dilation operator, GLCM (gray-level co-occurrence matrix), Hough transform and DBSCAN (density-based spatial clustering of applications with noise) for phases segmentation in the examples of certain material of eutectic HfB2-B4C ceramics. In the segmented regions, the further analysis for the microstructural elements is done with DBSCAN. The experimental results show that the proposed method achieves 95.75% segmentation accuracy for segmenting phases and 86.64% correct classification rate for the microstructure in the segmented phases. These experimental results show that our method is effective for the difficult task of the both segmentation and classification of the microstructural characteristics.


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How to Cite

Han, Y., Lai, C., Wang, B., Hu, T., Hu, D., & Gu, H. (2019). SEGMENTATION AND ANALYSIS METHOD FOR TWO-PHASE CERAMIC (HfB2-B4C) BASED ON THE DETECTION OF VIRTUAL BOUNDARIES. Image Analysis and Stereology, 38(1), 95–105.



Original Research Paper