Open AccessAuthenticated Medical Image Transmission using Enhanced Reversible Data Hiding (RDH) with NNP2 Algorithm and Rhombus Prediction
Author(s) -
R. Gomathi
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8847.118419
Subject(s) - computer science , histogram , information hiding , the internet , image (mathematics) , transmission (telecommunications) , medical diagnosis , steganography , distortion (music) , encryption , telemedicine , data transmission , rhombus , algorithm , artificial intelligence , computer security , computer network , medicine , mathematics , health care , telecommunications , bandwidth (computing) , amplifier , geometry , pathology , world wide web , economics , economic growth
During the last few years, the medical information of concerned patient is transferred from one doctor to another doctor via internet for better diagnosis and studies. Transferring medical information over a transmission medium is known as telemedicine. Telemedicine has been used to overcome distance barriers and to improve access to medical service. The telemedicine application includes emergency treatment, home monitor, military applications, and medical education. These medical images are corrupted by hackers when it is transferred through internet. Hence security of medical images is necessary. Watermarking is used for providing security while transferring medical images. Reversible Data Hiding (RDH) is one of the efficient methods for secure transmission of medical images. In this method, data hiding capacity is very small and the distortion level of recovers images is very large. To avoid these drawbacks, Nearest Neighborhood Pixel Prediction (NNP2 ) algorithm based on Chinese Remainder Theorem (CRT) is proposed and Rhombus Prediction is applied in NNP2 to increase data hiding capacity. The distortion level is reduced by Histogram Shifting. The performance of proposed method is evaluated using PSNR for number of medical images. The results shows that the proposed method gives good results when compared with traditional methods.