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Material Image Segmentation with the Machine Learning Method and Complex Network Method

Published online by Cambridge University Press:  14 January 2019

Chuanbin Lai ,
Leilei Song ,
Yuexing Han ,
Qian Li ,
Hui Gu ,
Bing Wang ,
Quan Qian  and
Wei Chen
Chuanbin Lai
Affiliation:
School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, CHINA, 200444;
Leilei Song
Affiliation:
School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, CHINA, 200444;
Yuexing Han*
Affiliation:
School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, CHINA, 200444; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, CHINA, 200444;
Qian Li
Affiliation:
Material Genome Institute & School of Materials Science and Engineering , Shanghai University , 99 Shangda Road, Shanghai, CHINA, 200444;
Hui Gu
Affiliation:
Material Genome Institute & School of Materials Science and Engineering , Shanghai University , 99 Shangda Road, Shanghai, CHINA, 200444;
Bing Wang
Affiliation:
School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, CHINA, 200444;
Quan Qian
Affiliation:
School of Computer Engineering and Science, Shanghai University, 99 Shangda Road, Shanghai, CHINA, 200444; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, CHINA, 200444;
Wei Chen
Affiliation:
Key Laboratory of Power Beam Processing, AVIC Manufacturing Technology Institute, Beijing, CHINA, 100024;
*
*[email protected]

Abstract

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The study of the relationship among the manufacturing process, the structure and the property of materials can help to develop the new materials. The material images contain the microstructures of materials, therefore, the quantitative analysis for the material images is the important means to study the characteristics of material structures. Generally, the quantitative analysis for the material microstructures is based on the exact segmentation of the materials images. However, most material microstructures are shown with various shapes and complex textures in images, and they seriously hinder the exact segmentation of the component elements. In this research, machine learning method and complex networks method are adopted to the challenge of automatic material image segmentation. Two segmentation tasks are completed: on the one hand, the images of the titanium alloy are segmented based on the pixel-level classification through feature extraction and machine learning algorithm; on the other hand, the ceramic images are segmented with the complex networks theory. In the first task, texture and shape features near each pixel in titanium alloy image are calculated, such as Gabor filters, Hu moments and GLCM (Gray-Level Co-occurrence Matrix) etc.. The feature vector for the pixel can be obtained by arraying these features. Then, classification is performed with the random forest model. Once each pixel is classified, the image segmentation is completed. In the second task, a complex network structure is built for the ceramic image. Then, a clustering algorithm of complex network is used to obtain network connection area. Finally, the clustered network structure is mapped back to the image and getting the contours among the component elements. The experimental results demonstrate that these methods can accurately segment material images.

Keywords

microstructurescanning electron microscopy (SEM)optical metallographycompound
Type
Articles
Information
MRS Advances , Volume 4 , Issue 19: Broader Impact/Machine Learning and Robotics , 2019 , pp. 1119 - 1124
Copyright
Copyright © Materials Research Society 2019 

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