Recent Team Achievement: Center-Surround Environmental Feature Model for Material Image Segmentation based on Machine Learning
Our team recently published a paper titled “Center-Environment Feature Models for Material Image Segmentation based on Machine Learning” in the international journal “Scientific Reports” (IF: 4.996). The School of Computer Engineering and Science at Shanghai University is listed as the first affiliation, with Associate Professor Han Yuexing as the first author and corresponding author. This work also received strong support from Professor Chen Qiaochuan, Professor Wang Bing, and Professor Liu Yi. Additionally, a significant contribution was made by Li Ruiqi and Yang Shen.
The performance of materials depends not only on their composition but also on their microstructure under various processing conditions. So far, the analysis of complex microstructural images has heavily relied on human expertise, lacking automated and quantitative characterization methods. Machine learning provides an emerging and essential tool for intelligently identifying various complex material phases. In this work, we propose a “Center-Environment Segmentation” (CES) feature model for image segmentation, which leverages machine learning techniques to segment images using annotated environmental features and domain knowledge. The CES model introduces neighborhood information as features for a given pixel, reflecting the relationships between the pixel of interest and its surrounding environment. An iterative ensemble machine learning approach is then employed to train and refine the image segmentation model. The CES model has been successfully applied to segment images of seven different materials with complex textures, including steel, wood, and others. In the study of steel image segmentation, the CES method demonstrates superior overall performance in determining boundary contours compared to many traditional methods. This work demonstrates that the iterative incorporation of domain knowledge and environmental features can enhance the accuracy of segmenting various complex material microstructure images.
Essay: Center-environment feature models for materials image segmentation based on machine learning