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De-identification of faces in 2D DICOM images

With the increasing resolution of MR and CT scans, it has become feasible to reconstruct detailed 3D images of faces.

Usually face de-identification in medical imaging is done after the reconstruction, i.e. in 3D (see references). Different techniques are used to this end including brain extraction, removal of facial features and deformation of the face surface.

In some scenarios this might not be adequate and it would be preferable removing or altering the facial features that contribute more to face recognition (by humans) in the original DICOM 2D slices. A review of the literature to determine which are those features would have to be performed by the student, but the eyes, mouth and nose seem the most probable candidates.

One possible approach is to use machine learning to identify those features and then remove or alter them without any of the brain pixels. Nevertheless, other approaches might be explored. The student will evaluate the different possibilities and implement a prototype. The development will be done preferably in Java to ease the integration with existing software.

It would be desirable that the software handles both CT and MR images, although this would not be a requirement for completion. The formal evaluation of how good the de-identification is would require the participation of human observers and thus would be done better later on, but it would be good if the student defines the experiments for such an evaluation.

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Project status: 
Still available
Degree level: 
MSc
Supervisors @ NeSC: 
Other supervisors: 
Trevor Carpenter
Subject areas: 
Machine Learning/Neural Networks/Connectionist Computing
Student project type: