Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems

Semiautomatic classification of cementitious materials using scanning electron microscope images

[+] Author Affiliations
Lucas Drumetz, Mauro Dalla Mura, Jocelyn Chanussot

Grenoble Institute of Technology, Image and Signal Department, Grenoble Images Speech Signals and Automatics Laboratory, 11 Rue des Mathématiques, Saint-Martin d’Hères 38402, France

Samuel Meulenyzer, Sébastien Lombard

Lafarge Centre de Recherche, 95 Rue du Montmurier, Saint-Quentin-Fallavier 38070, France

J. Electron. Imaging. 24(6), 061109 (Nov 12, 2015). doi:10.1117/1.JEI.24.6.061109
History: Received June 17, 2015; Accepted October 8, 2015
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Abstract.  Segmentation and classification are prolific research topics in the image processing community. These topics have been increasingly used in the context of analysis of cementitious materials on images acquired with a scanning electron microscope. Indeed, there is a need to be able to detect and to quantify the materials present in a cement paste in order to follow the chemical reactions occurring in the material even days after the solidification. We propose a new approach for segmentation and classification of cementitious materials based on the denoising of the data with a block-matching three-dimensional (3-D) algorithm, binary partition tree (BPT) segmentation, support vector machines (SVM) classification, and interactivity with the user. The BPT provides a hierarchical representation of the spatial regions of the data, allowing a segmentation to be selected among the admissible partitions of the image. SVMs are used to obtain a classification map of the image. This approach combines state-of-the-art image processing tools with user interactivity to allow a better segmentation to be performed, or to help the classifier discriminate the classes better. We show that the proposed approach outperforms a previous method when applied to synthetic data and several real datasets coming from cement samples, both qualitatively with visual examination and quantitatively with the comparison of experimental results with theoretical ones.

© 2015 SPIE and IS&T

Citation

Lucas Drumetz ; Mauro Dalla Mura ; Samuel Meulenyzer ; Sébastien Lombard and Jocelyn Chanussot
"Semiautomatic classification of cementitious materials using scanning electron microscope images", J. Electron. Imaging. 24(6), 061109 (Nov 12, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.061109


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