@conference {lien:79670M,
	title = {Open source tools for standardized privacy protection of medical images},
	booktitle = {Medical Imaging 2011: Advanced PACS-based Imaging Informatics and Therapeutic Applications},
	volume = {7967},
	number = {1},
	year = {2011},
	pages = {79670M},
	publisher = {SPIE},
	organization = {SPIE},
	abstract = {In addition to the primary care context, medical images are often useful for research projects and community healthcare networks, so-called "secondary use". Patient privacy becomes an issue in such scenarios since the disclosure of personal health information (PHI) has to be prevented in a sharing environment. In general, most PHIs should be completely removed from the images according to the respective privacy regulations, but some basic and alleviated data is usually required for accurate image interpretation. Our objective is to utilize and enhance these specifications in order to provide reliable software implementations for de- and re-identification of medical images suitable for online and offline delivery. DICOM (Digital Imaging and Communications in Medicine) images are de-identified by replacing PHI-specific information with values still being reasonable for imaging diagnosis and patient indexing. In this paper, this approach is evaluated based on a prototype implementation built on top of the open source framework DCMTK (DICOM Toolkit) utilizing standardized de- and re-identification mechanisms. A set of tools has been developed for DICOM de-identification that meets privacy requirements of an offline and online sharing environment and fully relies on standard-based methods.},
	doi = {10.1117/12.877989},
	url = {http://link.aip.org/link/?PSI/7967/79670M/1},
	author = {Chung-Yueh Lien and Onken, Michael and Marco Eichelberg and Tsair Kao and Andreas Hein},
	editor = {William W. Boonn and Brent J. Liu}
}
@article {19745435,
	title = {Reversible anonymization of DICOM images using automatically generated policies.},
	journal = {Studies in health technology and informatics},
	volume = {150},
	year = {2009},
	month = {2009},
	pages = {861-5},
	abstract = {Many real-world applications in the area of medical imaging like case study databases require separation of identifying (IDATA) and non-identifying (MDATA) data, specifically those offering Internet-based data access. These kinds of projects also must provide a role-based access system, controlling, how patient data must be organized and how it can be accessed. On DICOM image level, different image types support different kind of information, intermixing IDATA and MDATA in a single object. To separate them, it is possible to reversibly anonymize DICOM objects by substituting IDATA by a unique anonymous token. In case that later an authenticated user needs full access to an image, this token can be used for re-linking formerly separated IDATA and MDATA, thus resulting in a dynamically generated, exact copy of the original image. The approach described in this paper is based on the automatic generation of anonymization policies from the DICOM standard text, providing specific support for all kinds of DICOM images. The policies are executed by a newly developed framework based on the DICOM toolkit DCMTK and offer a reliable approach to reversible anonymization. The implementation is evaluated in a German BMBF-supported expert network in the area of skeletal dysplasias, SKELNET, but may generally be applicable to related projects, enormously improving quality and integrity of diagnostics in a field focused on images. It performs effectively and efficiently on real-world test images from the project and other kind of DICOM images.},
	author = {Onken, Michael and Riesmeier, J{\"o}rg and Engel, Marcel and Yabanci, Adem and Zabel, Bernhard and Despr{\'e}s, Stefan}
}
