ArticleJournal of imaging informatics in medicine2024
A Method for Efficient De-identification of DICOM Metadata and Burned-in Pixel Text.
Article in Journal of imaging informatics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- An automated end-to-end pipeline for the management, de-identification, and distribution of whole-slide images using DICOM: An institutional implementation.Journal of pathology informatics · 2026Article
- De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review.Healthcare informatics research · 2026Article
- Exploring AI-Based System Design for Pixel-Level Protected Health Information Detection in Medical Images.Journal of imaging informatics in medicine · 2026Article
- A Multi-Dimensional Framework for Data Quality Assurance in Cancer Imaging Repositories.Cancers · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
De-identification of DICOM images is an essential component of medical image research. While many established methods exist for the safe removal of protected health information (PHI) in DICOM metadata, approaches for the removal of PHI "burned-in" to image pixel data are typically manual, and automated high-throughput approaches are not well validated. Emerging optical character recognition (OCR) models can potentially detect and remove PHI-bearing text from medical images but are very time-consuming to run on the high volume of images found in typical research studies. We present a data processing method that performs metadata de-identification for all images combined with a targeted approach to only apply OCR to images with a high likelihood of burned-in text. The method was validated on a dataset of 415,182 images across ten modalities representative of the de-identification requests submitted at our institution over a 20-year span. Of the 12,578 images in this dataset with burned-in text of any kind, only 10 passed undetected with the method. OCR was only required for 6050 images (1.5% of the dataset).
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Registered trials
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