Automated segmentation of microtomography imaging of Egyptian mummies

• URL: http://arxiv.org/abs/2105.06738v1
• Date: Fri, 14 May 2021 09:56:13 GMT
• Title: Automated segmentation of microtomography imaging of Egyptian mummies
• Authors: Marc Tanti, Camille Berruyer, Paul Tafforeau, Adrian Muscat, Reuben Farrugia, Kenneth Scerri, Gianluca Valentino, V. Armando Sol\'e and Johann A. Briffa
• Abstract summary: We develop a tool to automatically segment 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. A qualitative analysis of the segmented output shows that our results are close in term of usability to those from deep learning, justifying the use of these techniques.
• Score: 3.8328962782003964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Propagation Phase Contrast Synchrotron Microtomography (PPC-SR${\mu}$CT) 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 term of usability to those from deep learning, justifying the use of these techniques.

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