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https://www.um.edu.mt/library/oar/handle/123456789/85896
Title: | Automated segmentation of microtomography imaging of Egyptian mummies |
Authors: | Tanti, Marc Berruyer, Camille Tafforeau, Paul Muscat, Adrian Farrugia, Reuben A. Scerri, Kenneth Valentino, Gianluca Solé, V. Armando Briffa, Johann A. |
Keywords: | Computer graphics Optical data processing Computer simulation Microcomputed tomography Human remains (Archaeology) Image segmentation Mummies -- Radiography -- Egypt |
Issue Date: | 2021 |
Publisher: | PLoS |
Citation: | Tanti, M., Berruyer, C., Tafforeau, P., Muscat, A., Farrugia, R., Scerri, K., ... & Briffa, J. A. (2021). Automated segmentation of microtomography imaging of Egyptian mummies. PLoS ONE, 16(12), e0260707. DOI: https://doi.org/10.1371/journal.pone.0260707 |
Abstract: | Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94–98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97–99%) at much lower complexity. A qualitative analysis of the segmented output shows that our results are close in terms of usability to those from deep learning, justifying the use of these techniques. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/85896 |
Appears in Collections: | Scholarly Works - FacICTCCE |
Files in This Item:
File | Description | Size | Format | |
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journal.pone.0260707.pdf | 7.12 MB | Adobe PDF | View/Open |
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