SEGMENTATION AND ANALYSIS METHOD FOR TWO-PHASE CERAMIC (HfB2-B4C) BASED ON THE DETECTION OF VIRTUAL BOUNDARIES
DOI:
https://doi.org/10.5566/ias.1992Keywords:
boundary detection, clustering algorithm, image segmentation, two-phase microstructure, virtual boundaryAbstract
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.
References
Ananyev M, Medvedev D, Gavrilyuk A, Mitri S, Demin A, Malkov V, Tsiakaras P (2014). Cu and Gd co-doped BaCeO 3 proton conductors: Experimental vs SEM image algorithmic-segmentation results. ELECTROCHIM ACTA 125:371-9.
Chermant JL (1986). Characterization of the microstructure of ceramics by image analysis. CERAM INT 12(2):67-80.
Cai HH, Cheng Y, Liu BX (2013). Ceramic microstructure image segmentation by mean shift. APPL MECH MATER 423:2602-5.
Chen Y, Chen J (2014). A watershed segmentation algorithm based on ridge detection and rapid region merging. Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference 420-4.
Diplaros A, Vlassis N, Gevers T (2007). A spatially constrained generative model and an EM algorithm for image segmentation. IEEE T NEURAL NETWOR 18(3):798-808.
Ester M, Kriegel HP, Sander J, Xu X (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd 96(34):226-31.
Haralick RM, Shanmugam K (1973). Textural features for image classification. IEEE T SYST MAN CYB 6:610-21.
Haha MB, Gallucci E, Guidoum A, Scrivener KL (2007). Relation of expansion due to alkali silica reaction to the degree of reaction measured by SEM image analysis. CEMENT CONCRETE RES 37(8):1206-14.
Hough PV (1962). Method and means for recognizing complex patterns. U.S. Patent 3,069,654.
Jeulin D, Maxime M (2008). Segmentation of 2D and 3D textures from estimates of the local orientation. IMAGE ANAL STEREOL 27(3): 183-92.
Lehar SM (2003). The world in your head: A gestalt view of the mechanism of conscious experience. Psychology Press 91-105.
Li H, Liao X, Li C, Huang H, Li C (2011). Edge detection of noisy images based on cellular neural networks. COMMUN NONLINEAR SCI 16(9):3746-59.
Lopez P, Lira J, Hein I (2015). Discrimination of Ceramic Types Using Digital Image Processing by Means of Morphological Filters. Archaeometry 57(1):146-62.
Muthukrishnan R, Radha M (2011). Edge detection techniques for image segmentation. International Journal of Computer Science & Information Technology 3(6):259.
Otsu N (1979). A threshold selection method from gray-level histograms. IEEE T SYST MAN CYB 9(1):62-6.
Osher S, Fedkiw R (2006). Level set methods and dynamic implicit surfaces. Springer Science & Business Media 153.
Serra J, Pierre SE (2012). Mathematical morphology and its applications to image processing. Springer Science & Business Media 2.
Todd CS, Heeschen WA (2016). Image Texture Analysis and Application to Acicular Mullite Porous Ceramic Microstructure. MICROSC MICROANAL 22(S3):544-5.
Vanderesse N, Maire E, Darrieulat M, Montheillet F, Moreaud M, Jeulin D (2008). Three-dimensional microtomographic study of Widmanstätten microstructures in an alpha/beta titanium alloy. SCRIPTA MATER 58(6):512-5.
Vales F, Rezakhanlou R, Olagnon C (1999). Determination of the fracture mechanical parameters of porous ceramics from microstructure parameters measured by quantitative image analysis. J MATER SCI 34(16):4081-8.
Xian GM (2010). An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. EXPERT SYST APPL 37(10):6737-41.
Yang Y, Guo Y (2009). Application of digital image technology to ceramic micro-image processing. Computer Engineering and Design 30(3):1008-9.
Yang Y, Guo Y, Wang X (2009). Digital Image Technology Assistant for the Quantitative Analysis of the Ceramics Microstructure. Materials Review 23(14):83-6.
Zhao Z, Ding GT, Fan ML, Zhang HR, Wang L, Chen L (2016). Material Image Segmentation Combined LBP Texture and Local Gray Level Feature. Computer Technology and Development 26(10):11-6.
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