Characterising the structure of cellular metals is a difficult task. The internal structure of cellular metals can be determined using micro-computed tomography (mCT). However, mCT scanning provides digital images in greyscale with various problematic artefacts. In addition, the grey intensity of cellular metals usually varies greatly due to the internal porosity of the material. Therefore, binary image segmentation to extract material segments from digital images is quite difficult. Our contribution can be summarised as follows. A comprehensive evaluation of various mixture models that have been shown in the literature to be useful for tomography, but for the purpose of binary image segmentation of cellular metals and internal porosity assessment. We propose a novel merging technique to merge different components of the mixture model for the purpose of binary image segmentation of cellular metals. Finally, to enforce spatial regularisation and further improve the binary image segmentation, we combine the obtained two-segment mixture model (material-void mixture model) with Markov random fields and evaluate the effects of different strengths of spatial regularisation. Our proposals are thoroughly investigated using five different types of cellular metals. The reported results are promising and competitive and speak in favour of the relevance of our proposals.

中文翻译：

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使用混合建模和马尔可夫随机场对 mCT 扫描的细胞金属进行无监督图像分割的指南

表征多孔金属的结构是一项艰巨的任务。细胞金属的内部结构可以使用微计算机断层扫描（mCT）来确定。然而，mCT 扫描提供的灰度数字图像存在各种有问题的伪影。此外，由于材料内部孔隙率的原因，多孔金属的灰度强度通常变化很大。因此，通过二值图像分割从数字图像中提取素材片段是相当困难的。我们的贡献可总结如下。对文献中显示的对断层扫描有用的各种混合模型的综合评估，但用于细胞金属的二值图像分割和内部孔隙率评估。我们提出了一种新颖的合并技术来合并混合模型的不同组件，以实现细胞金属的二值图像分割。最后，为了加强空间正则化并进一步改进二值图像分割，我们将获得的两段混合模型（材料-空隙混合模型）与马尔可夫随机场相结合，并评估不同强度的空间正则化的效果。我们的建议使用五种不同类型的多孔金属进行了彻底的研究。报告的结果是有希望的和有竞争力的，并且有利于我们提案的相关性。