Obscuring Surface Anatomy in Volumetric Imaging Data
- 971 Downloads
The identifying or sensitive anatomical features in MR and CT images used in research raise patient privacy concerns when such data are shared. In order to protect human subject privacy, we developed a method of anatomical surface modification and investigated the effects of such modification on image statistics and common neuroimaging processing tools. Common approaches to obscuring facial features typically remove large portions of the voxels. The approach described here focuses on blurring the anatomical surface instead, to avoid impinging on areas of interest and hard edges that can confuse processing tools. The algorithm proceeds by extracting a thin boundary layer containing surface anatomy from a region of interest. This layer is then “stretched” and “flattened” to fit into a thin “box” volume. After smoothing along a plane roughly parallel to anatomy surface, this volume is transformed back onto the boundary layer of the original data. The above method, named normalized anterior filtering, was coded in MATLAB and applied on a number of high resolution MR and CT scans. To test its effect on automated tools, we compared the output of selected common skull stripping and MR gain field correction methods used on unmodified and obscured data. With this paper, we hope to improve the understanding of the effect of surface deformation approaches on the quality of de-identified data and to provide a useful de-identification tool for MR and CT acquisitions.
KeywordsBiomedical imaging Facial recognition MR imaging CT imaging Privacy 3D
- Chen, J., Siddiqui, K., Moffitt, R., Juluru, K., Kim, W., Safdar, N., & Siegel, E. (2007). Observer success rates for identification of 3D surface reconstructed facial images and implications for patient privacy and security. Proceedings of SPIE International Society for Optical Engineering, 65161B-1–65161B-8.Google Scholar
- Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., & Marchal, G. (1995). Automated multi-modality image registration based on information theory. Information Processing in Medical Imaging, 263–274.Google Scholar
- Fennema-Notestine, C., Ozyurt, I. B., Clark, C. P., Morris, S., Bischoff-Grethe, A., Bondi, M. W., et al. (2006). Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Human Brain Mapping, 27(2), 99–113. doi: 10.1002/hbm.20161.PubMedCrossRefGoogle Scholar
- Jähne, B. (1997). Digital Image Processing (p. 622). Springer. Retrieved from http://www.amazon.com/Digital-Image-Processing-Bernd-J%C3%A4hne/dp/3540240357.
- Jarudi, I. N., & Sinha, P. (2003). Relative Contributions of Internal and External Features to Face Recognition. Retrieved from http://dspace.mit.edu/handle/1721.1/7274.
- Kaufman, A., & Shimony, E. (1987). 3D scan-conversion algorithms for voxel-based graphics. Proceedings of the 1986 workshop on Interactive 3D graphics - SI3D ’86 (pp. 45–75). New York, New York, USA: ACM Press. doi: 10.1145/319120.319126.
- Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007a). Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience, 19(9), 9.CrossRefGoogle Scholar
- Marcus, D. S., Olsen, T. R., Ramaratnam, M., & Buckner, R. L. (2007b). The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics, 5(1), 11–34.Google Scholar
- Mazura, J. C., Juluru, K., Chen, J. J., Morgan, T. A., John, M., & Siegel, E. L. (2011). Facial recognition software success rates for the identification of 3D surface reconstructed facial images: implications for patient privacy and security. Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology. doi: 10.1007/s10278-011-9429-3.
- Prior, F. W., Brunsden, B., Hildebolt, C., Nolan, T. S., Pringle, M., Vaishnavi, S. N., et al. (2009). Facial recognition from volume-rendered magnetic resonance imaging data. IEEE Transactions on Information Technology in Biomedicine: a Publication of the IEEE Engineering in Medicine and Biology Society, 13(1), 5–9. doi: 10.1109/TITB.2008.2003335.CrossRefGoogle Scholar