A physical model for microstructural characterization and segmentation of 3D tomography data

Brenne, Elise Otterlei; Dahl, Vedrana Andersen; Jørgensen, Peter Stanley

doi:10.1016/j.matchar.2020.110796

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…In addition, we again used only color values to estimate the mixture model. Although we consider this model to be quite robust for many examples, it still lacks some form of spatial coding to be even more robust, for example, the inclusion of local spatial information through the gradient intensity mixture model [62]. However, this requires further in-depth research.…”

Section: Conclusion and Future Considerationsmentioning

confidence: 99%

Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering

Panić,

Nagode,

Klemenc

et al. 2023

Mathematics

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The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be able to extract the desired information from images. In such cases, we are usually gently pushed towards the best possible clustering method, as it is often more robust than simple traditional image processing methods. We investigate the usefulness of combining two clustering methods for unsupervised image segmentation. We use the mixture models to extract the color and spatial image features based on the obtained output segments. Then we construct a similarity matrix (adjacency matrix) based on these features to perform spectral clustering. In between, we propose a label noise correction using Markov random fields. We investigate the usefulness of our method on many hand-crafted images of different objects with different shapes, colorization, and noise. Compared to other clustering methods, our proposal performs better, with 10% higher accuracy. Compared to state-of-the-art supervised image segmentation methods based on deep convolutional neural networks, our proposal proves to be competitive.

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Section: Conclusion and Future Considerationsmentioning

confidence: 99%

Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering

Panić,

Nagode,

Klemenc

et al. 2023

Mathematics

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show abstract

“…The FIB tomography approach consists of ablating a structure physically by FIB cutting, followed by a digital reconstruction based on SEM images taken after each ablation step. State-of-the-art FIB-SEM tomography can reduce voxel dimensions down to 3 × 3 × 3 nm 3 while the size of the analysed volume can still remain in the range of 10 × 10 × 10 µm 3 due to the automated process [27][28][29]. Such precise volume analysis offers very accurate 3D microstructural information in areas still large enough to provide a general depiction of the material.…”

Section: Introductionmentioning

confidence: 99%

The Microstructural Reconstruction of Variously Sintered Ni-SDC Cermets Using Focused Ion Beam Scanning Electron Microscopy Nanotomography

Kapun,

Majorovits,

Šturm

et al. 2024

Materials

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This work focuses in-depth on the quantitative relationships between primary first-order microstructural parameters (i.e., volume fractions of various phases and particle size distribution) with the more complex second-order topological features (i.e., connectivity of phases, three-phase boundary length (TPBL), interfacial areas, or tortuosity). As a suitable model material, a cermet nickel/samaria-doped ceria (Ni-SDC) is used as an anode in a solid oxide fuel cell (SOFC). A microstructure description of nano-sized Ni-SDC cermets, fabricated at various sintering conditions from 1100 °C to 1400 °C, was performed using FIB-SEM nanotomography. The samples were serially sectioned employing a fully automated slicing procedure with active drift correction algorithms and an auto-focusing routine to obtain a series of low-loss BSE images. Advanced image processing algorithms were developed and applied directly to image data volume. The microstructural–topological relationships are crucial for the microstructure optimisation and, thus, the improvement of the corresponding electrode performance. Since all grains of individual phases (Ni, SDC, or pores) did not percolate, special attention was given to the visualisation of the so-called active TPBL. Based on the determined microstructure characteristics of the prepared Ni-SDC cermets, including simulations of gas flow and pressure drop, thermal treatment at 1200 °C was recognised as the most appropriate sintering temperature.

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“…The segmentation step is a crucial part of the process of modelling cellular metals from CT scans [11]. Segmentation involves the separation of the regions of interest within the greyscale CT image, which in this case are the solid base material and the pores.…”

Section: Introductionmentioning

confidence: 99%

A Guide to Unsupervised Image Segmentation of Mct-Scanned Cellular Metals with Mixture Modelling and Markov Random Fields

Panić

Borovinšek

Vesenjak

et al. 2023

Preprint

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A physical model for microstructural characterization and segmentation of 3D tomography data

Cited by 6 publications

References 26 publications

Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering

Combining Color and Spatial Image Features for Unsupervised Image Segmentation with Mixture Modelling and Spectral Clustering

The Microstructural Reconstruction of Variously Sintered Ni-SDC Cermets Using Focused Ion Beam Scanning Electron Microscopy Nanotomography

A Guide to Unsupervised Image Segmentation of Mct-Scanned Cellular Metals with Mixture Modelling and Markov Random Fields

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