Open source tools for standardized privacy protection of medical images

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 deidentification that meets privacy requirements of an offline and online sharing environment and fully relies on standardbased methods.

Image Data Sharing for Biomedical Research—Meeting HIPAA Requirements for De-identification

“…Without a greater confidence in the image de-identification process, open-access DICOM archives that can be queried to correlate with genetics will never achieve their potential. Some international efforts besides those described in this paper are ongoing with the intent to achieve similar ends [25]. In the absence of community consensus on image de-identification and userfriendly tools and SOPs, researchers have been understandably reluctant to create publicly accessible image archives.…”

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

“…deface brain magnetic resonance images (MRI) as demonstrated in Fig. 2 For further analysis of a selection of tools highlighted in Table 1, we point to the analyses provided by Lien et al [47], Aryanto et al [48], and Gonzalez et al [38] that may further assist the interested reader to match specific tool requirements with specific tool capabilities.…”