Securing Medical Images for Telemedicine Applications

Abstract

Due to the advancements in distributed computing, storage devices, and imaging tools, images are communicated over public networks. They are prone to various security threats such as eavesdropping, illegal modification, duplication. Therefore, efficiently securing the images has received much more attention in the last few years. Internet of Medical Things (IoMT) plays a vital role in healthcare systems to increase the accuracy, reliability, and productivity of electronic devices. Medical images have an ambitious role in today's health care systems for diagnostic or forensic purposes. Medical images are sensitive to any change in their contents, where minor changes could lead to erroneous diagnosis. Recently, image processing applications have enabled malicious modification of image contents. Zero-watermarking is a well-known watermarking approach that does not cause any degradation in the host image, which is a good choice for sensitive data security, integrity preservation, and ownership protection. Zero-watermarking is widely used in medical image securing; however, its robustness against geometric attacks is doubtful. Thesis proposes a fast multiple zero-watermarking approach for medical image security and copyright protection in telemedicine application without deforming the original image. The proposed method is based on the most significant features generated from Multi-channel Fractional Legendre Fourier Moments (MFrLFMs) and the two-dimensional Discrete Henon Map. We implemented the proposed method with low memory capacity and computational power Raspberry Pi Linux microprocessor. The proposed algorithm was tested on various medical images to test the robustness against geometric attacks and common signal processing attacks. Experimental results show that the proposed method achieves better values of bit error rate (BER), normalized correlation (NC), and structural similarity index measure (SSIM) compared with recently published works. The proposed approach is robust against different image processing and geometric attacks compared with other state-of-the-art approaches. The experimental results showed that the proposed algorithm outperforms the most recent research after comparing for different geometric and signal processing attacks. For geometric attacks (rotation by 35o), the BER (0.00241) was lower compared with others. Similarly, the NC (0.99643) was higher than other. Also, the average computation time for the proposed method 22.716 seconds for generation phase and 23.234 seconds for verification.