A Novel Image Segmentation Approach for Microstructure Modelling
A Novel Image Segmentation Approach for Microstructure Modelling
复制标题
一种用于微观结构建模的新型图像分割方法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Marshall
中科院分区：
文献类型：
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作者：
M. Watson;M. Marshall
微观结构模型用于在给定微观结构图像的情况下研究材料的整体特性。通过它们的使用，可以独立研究微观结构特征的影响。然后可以优化工艺，以提供所需的微观结构特征选择。目前自动分割 SEM 图像的方法要么错过裂缝，导致性能大幅高估，要么使用不合理的方法来选择将裂缝分类为孔隙率的阈值，导致孔隙率的高估。在这项工作中，提出了一种新颖的自动图像分割方法，该方法为微观结构中的每个相和裂纹的附加相生成图。阈值点的选择基于每个相位的亮度值应呈正态分布的假设。该图像分割方法已与其他可用方法进行了比较，并且表明与相关替代方法相比，随着输入图像的亮度和对比度的变化，其可重复性相同或更高。由此产生的建模路线能够在实验误差范围内预测密度和比热，同时观察到热导率的预期预测不足。
Microstructure models are used to investigate bulk properties of a material given images of its microstructure. Through their use the effect of microstructural features can be investigated independently. Processes can then be optimised to give the desired selection of microstructural features. Currently automatic methods of segmenting SEM images either miss cracks leading to large overestimates of properties or use unjustifiable methods to select a threshold point which class cracks as porosity leading to over estimates of porosity. In this work, a novel automatic image segmentation method is presented which produces maps for each phase in the microstructure and an additional phase of cracks. The selection of threshold points is based on the assumption that the brightness values for each phase should be normally distributed. The image segmentation method has been compared to other available methods and shown to be as or more repeatable with changes of brightness and contrast of the input image than relevant alternatives. The resulting modelling route is able to predict density and specific heat to within experimental error, while the expected under predictions for thermal conductivity are observed.