Evolutionary programming approach for securing medical images using genetic algorithm and standard deviation

Abstract

The integrity and security of medical data have become a big challenge for healthcare services applications. Images of hidden text represent steganography forms in which the image is exploited as an object to cover information and data. So, the data masking ability and image quality of the cover object are significant elements in image masking. In this study, the patient’s personal information and message generated by the doctor’s comment are stored in the images. Image pixels and message bits are exchanged sequentially. The best cluster is randomly selected using the genetic algorithm (GA) and standard deviation (STD) methods. The method, depending on optimizing and taking benefits of the similarities between pixels, has been proposed. High image quality can be achieved by using stego-image and increasing the data amount to be hidden. Analysis metrics of visual quality such as peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and bit error rate (BER) are adopted to assess the performance of the method proposed. The suggested and proposed model has proven its capability to mask the secrete data of patients into a cover image transmitted with high ability, imperceptibility, and minimal future degradation.