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. 2023 Feb 8:6:100319.
doi: 10.1016/j.fsisyn.2023.100319. eCollection 2023.

Preparing computed tomography images for machine learning in forensic and virtual anthropology

Martin Lo  1   2 , Enrico Mariconti  1 , Sherry Nakhaeizadeh  1   2 , Ruth M Morgan  1   2
Affiliations

Preparing computed tomography images for machine learning in forensic and virtual anthropology

Martin Lo et al. Forensic Sci Int Synerg. .
No abstract available

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Pre-processing pipeline showing the steps for adapting CT (DICOM) datasets into file formats suitable for 2D and 3D image processing, virtual modelling, and machine learning work in forensic anthropology. The first stage is retrieving the data (Data Source), followed by the operations of data cleaning (Restructuring the data); the last stage is the Useable output, where different scripts prepare the scans for outputs required for the specifics of different types of research.
Fig. 2
Fig. 2
Uncollated DICOM files within individual CT study folder.
Fig. 3
Fig. 3
DICOM files automatically sorted and extracted into subfolders matching the series number in the metadata of each DICOM file.
Fig. 4
Fig. 4
DICOM file with sliding table highlighted in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5
Fig. 5
DICOM file labelled ‘Original Image’ undergoes a series of thresholding and masking functions to return an area of interest labelled as ‘Mask’. The ‘Final Image’ returned overwrites into the DICOM file, thus removing noisy structures such as the sliding gantry.
Fig. 6
Fig. 6
Noise removal and segmentation script on CT scan from the NMDID.
Fig. 7
Fig. 7
Noise removal and segmentation script on CT scan from the TCIA.
See this image and copyright information in PMC

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