20.500.12556/RUL-154730 A guide to unsupervised image segmentation of mCT-scanned cellular metals with mixture modelling and Markov random fields 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. mixture models spatial regularisation Markov random fields cellular metals porosity mešani models prostorska regularizacija markovska naključna polja celične strukture poroznost true false true Angleški jezik Slovenski jezik Članek v reviji 2024-02-26 13:47:07 2024-02-26 13:47:13 2025-02-06 13:45:42 0000-00-00 00:00:00 2024 0 0 13 str. Vol. 239, art. 112750 Mar. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 620.1 1873-4197 10.1016/j.matdes.2024.112750 186796035 1-s2.0-S0264127524001229-main.pdf 1-s2.0-S0264127524001229-main.pdf 1 47B9343B4D05216824A79DA69CEFC412 3cedffae0a3c16e5c15ec395e36f34a9b250970708862fbf1c12125fbc08246d 4d5e2acb-b242-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196508 https://www.sciencedirect.com/science/article/pii/S0264127524001229 1 2abd20aa-d4a5-11ee-b5a4-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=181270 Fakulteta za strojništvo 0 0 0