数字材料图像的多相分割

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2023-02-02 DOI:10.1017/dce.2022.40
R. Saxena, R. Day-Stirrat, Chaitanya Pradhan
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引用次数: 1

摘要

孔隙尺度特征的多相分割和材料数字图像的矿物学识别对于自然资源领域的许多应用至关重要。然而,所涉及的材料(岩石、催化剂颗粒和合成合金)具有复杂和不可预测的成分。可以扩展用于这些材料图像的多相分割的算法相对较少,并且非常耗费人力。挑战在于设计与上下文无关的算法,可以使用较少的训练数据,并且可以处理材料组成的不可预测性。在训练数据有限的情况下,半监督算法在分类方面取得了成功;除了有标记的数据外,他们还使用未标记的数据来进行分类。所获得的分割比完全监督学习方法更准确。这项工作提出了一种半监督聚类算法,称为连续迭代制导光谱类拒绝(CIGSCR),用于材料数字扫描的多相分割。CIGSCR利用光谱内聚,将输入图像的强度直方图分解成簇。这种分割为分类策略提供了基础,分类策略可以作为获得最终分割的后处理步骤来实现。提出了一种分类策略。岩石的微型计算机断层扫描被用来呈现结果。实验证明,CIGSCR能够成功地区分特征,直至灰度值的唯一性,并提取出完整图像堆栈(3D)中存在的特征,包括训练数据中未出现的特征。结果包括成功的实例和局限性。简要讨论了数据大小$ \mathcal{O}\left({10}^9\right) $体素的可伸缩性。
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Multiphase segmentation of digital material images
Abstract Multiphase segmentation of pore-scale features and identification of mineralogy from digital images of materials is critical for many applications in the natural resources sector. However, the materials involved (rocks, catalyst pellets, and synthetic alloys) have complex and unpredictable composition. Algorithms that can be extended for multiphase segmentation of images of these materials are relatively few and very human-intensive. Challenges lie in designing algorithms that are context free, can function with less training data, and can handle the unpredictability of material composition. Semisupervised algorithms have shown success in classification in situations characterized by limited training data; they use unlabeled data in addition to labeled data to produce classification. The segmentation obtained can be more accurate than fully supervised learning approaches. This work proposes using a semisupervised clustering algorithm named Continuous Iterative Guided Spectral Class Rejection (CIGSCR) toward multiphase segmentation of digital scans of materials. CIGSCR harnesses spectral cohesion, splitting the intensity histogram of the input image down into clusters. This splitting provides the foundation for classification strategies that can be implemented as postprocessing steps to get the final segmentation. One classification strategy is presented. Micro-computed tomography scans of rocks are used to present the results. It is demonstrated that CIGSCR successfully enables distinguishing features up to the uniqueness of grayscale values, and extracting features present in full image stacks (3D), including features not presented in the training data. Results including instances of success and limitations are presented. Scalability to data sizes $ \mathcal{O}\left({10}^9\right) $ voxels is briefly discussed.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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