ROI Based Medical Image Watermarking with Zero Distortion and Enhanced Security

Abstract

Medical Image Watermarking has been considered as an appropriate method for enhancing the data security and content verification in medical science. For the purpose of achieving better security it is recommended to hide the data in the medical image itself that will improve the reliability of medical information system. In our paper, the work is divided in two main stages, first of all image is divided into two parts: Region of Interest that is defined as informational part of image and Region of Non Interest that is defined as noninformational part. In our work we have used Region of Non Interest to hide the data so that the informational part of the image kept safe. Watermark is encrypted by using RSA. A Discrete Wavelet Transform based approach is used for embedding the encrypted data in Region of Non Interest part of image for enhancing data security. The experimental result shows that watermark embedded by the proposed algorithm provides better security and can survive successfully under different attacks.

Advances in medical image watermarking: a state of the art review

“…The state-of-the-art techniques along with evaluation parameters, pros, and cons of the region-based watermarking techniques for medical image watermarking have been presented in Table 7 [ 2 , 17 , 35 , 42 , 45 , 55 , 62 , 74 , 77 , 80 ].…”

Embed Watermark Computing Using Logistic Map Chaotic System For Securing Medical Images

“…We inverse the procedures of embedding to extract watermark as following steps (Neha Solanki et al, 2014) : 1) Extract the ROI from the received watermarked image depend on Xmax, Xmin, Ymin and Ymax parameter provided with watermarked image. 2) Apply the 4-level Haar-lifting wavelet transform to RONI, which results in a image approximation at 4-level and sequence of images horizontal, vertical, and diagonal details at each of 4-leveldecomposition.…”

An Enhanced Digital Image Watermarking Scheme for Medical Images using Neural Network, DWT and RSA

“…Section IV describes the stimulation model. Results and comparisons are presented in section V. Finally, section VI describes conclusion and future scope [17].…”

“…The strength of the proposed embedding scheme is also supported by numerous image quality matrices. Solanki, N. et al [17] proposed an algorithm in which work is divided in two main stages; initially image is divided into two parts: Region of Interest i.e. defined as informational piece of image and Region of Non Interest i.e.…”