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DC Field | Value | Language |
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dc.contributor.author | Tanti, Marc | - |
dc.contributor.author | Berruyer, Camille | - |
dc.contributor.author | Tafforeau, Paul | - |
dc.contributor.author | Muscat, Adrian | - |
dc.contributor.author | Farrugia, Reuben A. | - |
dc.contributor.author | Scerri, Kenneth | - |
dc.contributor.author | Valentino, Gianluca | - |
dc.contributor.author | Solé, V. Armando | - |
dc.contributor.author | Briffa, Johann A. | - |
dc.date.accessioned | 2021-12-21T09:32:56Z | - |
dc.date.available | 2021-12-21T09:32:56Z | - |
dc.date.issued | 2021 | - |
dc.identifier.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 | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/85896 | - |
dc.description.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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | PLoS | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Computer graphics | en_GB |
dc.subject | Optical data processing | en_GB |
dc.subject | Computer simulation | en_GB |
dc.subject | Microcomputed tomography | en_GB |
dc.subject | Human remains (Archaeology) | en_GB |
dc.subject | Image segmentation | en_GB |
dc.subject | Mummies -- Radiography -- Egypt | en_GB |
dc.title | Automated segmentation of microtomography imaging of Egyptian mummies | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1371/journal.pone.0260707 | - |
dc.publication.title | PLoS ONE | en_GB |
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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