Medical image watermarking with DWT-BAT algorithm

Medical images communicate imperative information to the doctors about a patient's health situation. Internet broadcasts these medical images to inaccessible sites of the globe which are inspected by specialist doctors. But data transmissions through unsecured web invoke validation problems for any image data. Medical images that are transmitted through the internet must be watermarked with patient pictures for substantiation by the doctors to ascertain the medical image. Watermarking medical images necessitate attentive adjustments to protect the information in the medical images with patient image watermarks. The medical images are used as an envelope image in the watermarking process which is visible on the network. These envelope medical images are watermarked with patient images in wavelet domain there by using the BAT algorithm form optimizing the embedding process for peak signal to noise ratio (psnr) and normalized cross correlation coefficient (ncc) values. The medical image envelope and letter inside envelope i.e. watermark image are transformed into wavelet domain and are mixed using scaling factor alpha which is termed as embedding strength. BAT algorithm is an optimization algorithm specialized in optimizing the values of peak-signal-to-noise ratio for a particular value of alpha, the embedding watermark strength. Finally these watermarked medical images are put on the network along with the secret key that will be used for extraction. At the receiving the embedded watermark is extracted using 2DWT using the embedding strength value using BAT algorithm. The robustness of the proposed watermarking techniques is tested with various attacks on the watermarked medical images. Peak-Signal-to-Noise ratios and Normalized cross correlation coefficients are computed to accesses the quality of the watermarked medical images and extracted patient images. The results are produced for three types of medical images with one patient image watermarks using single key by using four wavelets (haar, db, symlets, bior) at four different levels.