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1. #CREATE MY OWN DICOMDIR FILE WITH EXTRA TAGS SERIES#
2. #CREATE MY OWN DICOMDIR FILE WITH EXTRA TAGS FREE#

The tools for anonymizing the DICOM files identified by were separated into two groups: those allowing the de-identification of metadata and pixel data, and those allowing the de-identification of metadata only.

In order to choose the appropriate tool, we tested them and shared the results in this article.

#CREATE MY OWN DICOMDIR FILE WITH EXTRA TAGS FREE#

We therefore investigated what resources were currently available and, thanks to, became aware of free tools available for anonymizing the metadata and pixel data of a DICOM file. Testing de-identification toolsįor one of our R&D projects, we needed to de-identify the DICOM files before using the CT images in a classification algorithm. In this article we will investigate the de-identification of DICOM files (metadata and pixel data) and the free tools available on the market to achieve this. There are many tools to anonymize the DICOM "tags" containing the metadata associated with the image, but few also allow the anonymization of the "pixel data". If the image includes annotations, the de-identification must also be done by "cleaning" the pixels of the image. In addition to this process, you must ensure that the image itself does not contain any personal data, especially when annotations are burned into the image (called “ burned in annotations”). You will find details regarding these "tags" and the various confidentiality profiles recommended by the DICOM PS 3.15 standard: Appendix E in this article. In order to use them in a computer vision project, it is essential that they be anonymized because they contain Personal Health Information (PHI).Īs seen in a previous article, the anonymization or de-identification of DICOM files involves processing information from several "tags" in the file that may contain personal patient information. Commonly, medical images are stored in DICOM format in the PACS of the health facility where they were collected. CT images, MR images) for a computer vision project using deep learning methods.

#CREATE MY OWN DICOMDIR FILE WITH EXTRA TAGS SERIES#

In a series of articles, we will focus on the data preprocessing of medical images (e.g. Medical Imaging data preprocessing in AI: An essential step It consists of several steps that vary depending on the type of project you are going to conduct and the type of data you have. Data preprocessing is an important and mandatory step for any good data scientist to take when embarking on a data analysis project.