Anonymization tools¶
Sanitization of headers/filenames¶
see http://www.researchgate.net/post/Best_free_tool_for_DICOM_data_anonymization discussion on sanitization of DICOM headers
DeID (see paper), which provides an interactive tool for inspection and sanitization of Analyze and NIfTI images
PyDICOM’s deid, the “best effort anonymization for medical images using python” assists in filtering out DICOM fields and also masking out actual image data
Elimination of facial (and dental) features¶
Skull stripping¶
One of the approaches is perform complete skull stripping, e.g. using
3dSkullStrip of AFNI
Some dedicated de-identification tools work on this principle, e.g. DeID
Faces/dental stripping¶
More “gentle” approach is to strip out only the areas of face/mouth leaving skull, which might be important for some types of analysis. Usually achieved through alignment of pre-crafted mask to the research participants anatomy and removing of the masked out regions.
BIDSonym - a BIDS app interfacing a number of methods (pydeface, quickshear, mri_deface) listed below
https://github.com/hanke/gumpdata/blob/master/scripts/conversion/convert_dicoms_anatomy#L26
Rendering faces unrecognizable¶
Even more data/information preserving approach is to just obscure facial features in the anatomical images:
Obscuring Surface Anatomy in Volumetric Imaging Data Used for HCP data, using the Face Masking <https://nrg.wustl.edu/software/face-masking/> tool.
