3D RECONSTRUCTION AND ANALYSIS OF THE FRAGMENTED GRAINS IN A COMPOSITE MATERIAL
DOI:
https://doi.org/10.5566/ias.v32.p107-115Keywords:
clustering, damage, mathematical morphology, reconstruction, segmentation, solid propellantAbstract
X-ray microtomography from solid propellant allows studying the microstructure of fragmented grains in damaged samples. A new reconstruction algorithm of fragmented grains for 3D images is introduced. Based on a watershed transform of a morphological closing of the input image, the algorithm can be used with different sets of markers. Two of them are compared. After the grain reconstruction, a multiscale segmentation algorithm is used to extract each fragment of the damaged grains. This allows an original quantitative study of the fragmentation of each grain in 3D. Experimental results on X-ray microtomographic images of a solid propellant fragmented under compression are presented and validated.
References
Angulo J, Jeulin D (2007). Stochastic watershed segmentation. In: Proceedings of the International Symposium on Mathematical Morphology 8 (ISMM), vol. 1.
Beucher S (1994). Watershed, hierarchical segmentation and waterfall algorithm. In: Mathematical Morphology And Its Applications To Image Processing, vol. 2.
Beucher S, Lantuéjoul C (1979). Use of watersheds in contour detection. In: Proceedings of the International workshop on image processing, real-time edge and motion detection.
Beucher S, Marcotegui B (2009). P algorithm, a dramatic enhancement of the waterfall transformation. Tech. rep., CMM/Mines Paristech.
Faessel M, Jeulin D (2010). Segmentation of 3d microtomographic images of granular materials with the stochastic watershed. Journal of Microscopy 239:17–31.
Gillibert L, Jeulin D (2011a). 3d reconstruction of fragmented granular materials. In: Procedings of the 13th International Congress for Stereology, ICS-13.
Gillibert L, Jeulin D (2011b). Stochastic multiscale segmentation constrained by image content. In: Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing, ISMM’11. Berlin, Heidelberg: Springer-Verlag.
Jeulin D (1991). Modèles morphologiques de structures aléatoires et de changement d’échelle. Ph.D. thesis, University of Caen, France.
Jeulin D (2008). Remarques sur la segmentation probabiliste. Tech. Rep. N-10/08/MM, CMM/Mines Paristech.
Lloyd SP (1982). Least squares quantization in pcm. IEEE Transactions on Information Theory 28:129–37.
MacQueen JB (1967). Some methods for classification and analysis of multivariate observations. In: Cam LML, Neyman J, eds., Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press.
Matheron G (1967). Eléments pour une théorie des milieux poreux. Masson, Paris.
Noyel G, Angulo J, Jeulin D (2007). Random germs and stochastic watershed for unsupervised multispectral image segmentation. In: Proceedings of Knowledge-Based Intelligent Information and Engineering Systems (KES).
Otsu N (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man and Cybernetics 9:62–6.
Serra J (1982). Image Analysis and Mathematical Morphology. Academic Press, London.
Stawiaski J, Meyer F (2010). Stochastic watershed on graphs and hierarchical segmentation. In: Proceedings ECMI 2010.