Obscuring Surface Anatomy in Volumetric Imaging Data
Open accessAbstract
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.
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Data from prior OASIS projects have been extensively used in the development of neuroimaging tools, models of healthy aging and AD timecourse, hypothesis-driven questions, and educational/training purposes.
“Data from these projects have been extensively used in the development of neuroimaging tools, such as brain atlases 3,4 , tissue segmentation algorithms 5 , and de-identification tools 6 , used to create models of healthy aging 7,8 and AD timecourse 7-11 , used to answer hypothesis driven questions 12,13 , and used for educational and training purposes.”
From OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019) · cites this paper as [6]
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- Authors
- Milchenko, M., & Marcus, D.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Milchenko, Mikhail
- Venue
- Neuroinformatics
- Published
- 2012-09-12
- Volume
- 11
- Issue
- 1
- Pages
- 65-75
- ISSN
- 1539-2791 (ISSN-L)
- 1559-0089
- PMC
- PMC3538950
- OpenAlex
- W1983022940
- Cited by
- 145
- References
- 19
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 19 · 2023: 15 · 2022: 15 · 2021: 23 · 2020: 11 · 2019: 14 · 2018: 13 · 2017: 6 · 2015: 6 · 2014: 1 · 2013: 3
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Neuroinformatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (P30NS048056)
- National Institute of Biomedical Imaging and Bioengineering (R01EB009352)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- de-identification, patient privacy, skull stripping, surface deformation
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Brain, Child, Confidentiality, Face, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Middle Aged, Organ Size, Pattern Recognition, Automated, Skull, Tomography, X-Ray Computed, Research Subjects, Young Adult, Biometric Identification, Neuroimaging
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