Digital watermarking : applicability for developing trust in medical imaging workflows state of the art review

Qasim, AF; Meziane, F; Aspin, R

Abstract

Medical images can be intentionally or unintentionally manipulated both within the secure medical system environment and outside, as images are viewed, extracted and transmitted. Many organisations have invested heavily in Picture Archiving and Communication Systems (PACS), which are intended to facilitate data security. However, it is common for images, and records, to be extracted from these for a wide range of accepted practices, such as external second opinion, transmission to another care provider, patient data request, etc. Therefore, confirming trust within medical imaging workflows has become essential. Digital watermarking has been recognised as a promising approach for ensuring the authenticity and integrity of medical images. Authenticity refers to the ability to identify the information origin and prove that the data relates to the right patient. Integrity means the capacity to ensure that the information has not been altered without authorisation.
This paper presents a survey of medical images watermarking and offers an evident scene for concerned researchers by analysing the robustness and limitations of various existing approaches. This includes studying the security levels of medical images within PACS system, clarifying the requirements of medical images watermarking and defining the purposes of watermarking approaches when applied to medical images.

Citation

Qasim, A., Meziane, F., & Aspin, R. (2017). Digital watermarking : applicability for developing trust in medical imaging workflows state of the art review. Computer Science Review, 27, 45-60. https://doi.org/10.1016/j.cosrev.2017.11.003

• Journal Article Type: Article
• Acceptance Date: Nov 30, 2017
• Online Publication Date: Dec 13, 2017
• Publication Date: Dec 13, 2017
• Deposit Date: Nov 30, 2017
• Publicly Available Date: Dec 13, 2018
• Journal: Computer Science Review
• Print ISSN: 1574-0137
• Publisher: Elsevier
• Volume: 27
• Pages: 45-60
• DOI: https://doi.org/10.1016/j.cosrev.2017.11.003
• Publisher URL: http://dx.doi.org/10.1016/j.cosrev.2017.11.003
• Related Public URLs: https://www.journals.elsevier.com/computer-science-review/