Robust Embedding of Patient ID in Medical Images for Telemedicine Applications

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Sunita V. Dhavale


Most of the researchers have been researching algorithm to insert high information in the original medical image but increasing the size of embedded information affects the quality of watermarked medical image severely. This paper presents a new robust image barcode watermarking scheme for medical images that embeds human readable bar-coded watermark image which is generated from corresponding unique patient identification number (PID). Here only PID barcode image is embedded in a medical image for telemedicine applications instead of embedding whole Electronic Patient Record (EPR) data. Further, the presence of PID barcode watermark can be identified with or without manual intervention. A PID sequence watermark is represented as bar-coded binary logo image and embedded in the corresponding medical image. This in turn authenticates corresponding medical images and also helps in faster retrieval of whole patient data from the centrally managed hospital database. The proposed scheme first extracts the energy of DCT blocks from the host image and based on these energy values, the blocks are then classified into two clusters named High Energy Blocks (HEBs) and Low Energy Blocks (LEBs) using KMeans clustering technique. To achieve imperceptibility, the watermark bits are embedded only in HEBs leaving LEBs intact. By using the desirable characteristics of proposed technique, the imperceptibility requirements of watermarks are fulfilled along with increased robustness. Experimental results show that the proposed scheme is robust against common attacks and offers both objective as well as subjective watermark detection. The revealed watermark can be easily recognized by human eyes, even if the host image has undergone severe attacks. Due to inherent error detecting capability of the barcode watermark image, the watermark can be fully recovered against various kinds of attacks.

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How to Cite
DHAVALE, Sunita V.. Robust Embedding of Patient ID in Medical Images for Telemedicine Applications. ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY, [S.l.], v. 3, n. 3, aug. 2017. ISSN 2350-1146. Available at: <>. Date accessed: 21 oct. 2017.