Digital biosignals like digital radiological images and one-dimensional datasets are massively produced everyday in medical facilities around the world. The interoperability requirement for ubiquitous personalized health services has led to the creation of the electronic health record (EHR) and of the necessary exchange protocols. Generally, the EHR contains patient demographics, diagnostic reports or codes, prescriptions as well as radiological images. One of the most exciting future expectations for the EHR is its worldwide availability from any medical facility at any time.
In this context, healthcare systems have to upgrade from individually (personal or hospital) based towards more global e-health platform systems. These future personal e-health platforms, due to the strict ethics and legislative rules concerning medical data, have to ensure confidentiality, reliability (authenticity and integrity check), availability, and safe transfer of the medical files [1
]. Apart from legislation issues, the quality of the biomedical images has to remain intact to ensure a correct diagnosis. Thus, an automated framework is needed on these e-health platforms to verify the authenticity and integrity of the digital medical images.
Various methods have been widely used to protect digital multimedia files [4
]. Among them is digital watermarking that has been proposed and has been also used in the medical field for radiological images [2
]. The watermark could be a serial number, like the patient’s insurance code, or a hospital logo, or it could include parts or all of the EHR, textual files containing diagnosis, blood test profiles or other biosignals [1
]. Moreover, the watermarked image still conforms to the Digital Imaging and Communications in Medicine (DICOM) format.
In a medical image during diagnosis, the area of the image that contains the clinical finding constitutes the region of interest (ROI) and it is the one that should be preserved intact. The rest of the part that does not contain any clinical findings is the region of non-interest (RONI) [1
]. In many works, the watermark is inserted into the RONI, in that way, it leaves the ROI intact without any imperceptible modifications [1
Watermarking can be divided into many categories. Concerning the embedding space, they are categorized into spatial and frequency domain [5
]. In the spatial domain, the watermark is inserted by modifying the pixel values or more often the least significant bit [2
]. In the frequency domain, the watermark is embedded in some transformed signal coefficients (frequency bands) [1
In order to retrieve the original image, lately, reversible watermarking (RW) schemes have been introduced. These techniques not only provide protection by embedding the watermark into the original signal but also can recover the original image from the suspected one [9
]. Usually, in RW techniques, the RONI is utilized as the embedding area. In such cases, the quality of the RW technique depends highly on the embedding capacity of the RONI. Some authors define RONI as the region of background e.g., the black area inside an X-ray or a magnetic resonance imaging (MRI) image, or as any other nonsignificant area of the image. These methods belong to the data hiding
category, where the capacity of the carrier is a very important issue.
In the medical field, some newly developed approaches exist for the protection of MRI images. Medical watermarking has been applied in medical MRI images [1
]. In order to ensure that the original radiological image can be retrieved in its initial form, reversible watermarking techniques have been introduced [2
]. The basic requirements of a reversible data watermarking technique are robustness, imperceptibility, high embedding capacity, readily embedding, and retrieving.
In a work of Coatrieux et al. [10
], a mixed reversible scheme was proposed for head MRI images. In the recoverable image tamper proofing approach of Chuang and Chang [8
] that employs data compression, the host image was divided into blocks of equal sizes. To recover the “tampered” areas, every block of the host image is compressed by vector quantization to generate the recovery data. In Giakoumaki et al. [7
], an area of polygon was chosen to represent the ROI of a medical DICOM image. This ROI remains intact and the watermarking is inserted inside the RONI by difference expansion of adjacent pixel values. In a work by Shih and Wu [11
], the ROI is defined as a rectangle inside the original image, which is compressed in a lossless manner and the rest of the image that surrounds the ROI is lossy compressed. The watermark is embedded in the RONI area in the frequency domain.
In the previously described methods, the selection of the RONI is varying. In [1
], the ROI is selected manually or automatically as a rectangle. In [10
], a simple thresholding method operates in 2
2 pixel blocks to define the ROI with good detail. In [11
], the ROI is compressed by lossless compression while the rest, by lossy compression. In our work, a reversible RONI watermarking technique has been implemented for a sequence of brain MRI images. We chose to define ROI first as an area that contains the whole head shape [12
]. The ROI itself is the watermark. We consider that it is of importance to preserve the whole brain image for future diagnostic purposes [13
]. Moreover, a possible undetected indication in another area not characterized as malicious at the first place could be proven otherwise in a future diagnosis. The embedding capacity is increased by using the total RONI-embedding capacity from all the MRI slice sequences. An automated algorithm detects the available embedding space for each slice and calculates the total embedding capacity. The exact methodology is explained in the Methods
section. The results of the proposed method are given in the Results and Discussion
section, and the conclusions are drawn in the Conclusions