ROI-Fuzzy Based Medical Data Authentication Scheme for Smart Healthcare System

Abstract

During the current pandemic, telemedicine applications encourage radiologists to consult patients remotely. Medical images are typically sent over the internet for easy access when diagnosing a patient. Intruders can manipulate these images during communication. Therefore, we require a method of authentication for these images that is capable of authenticating these images before diagnosis. To address these issues, a watermarking scheme has been developed within the scope of this paper. The foundation of anticipated watermarking system is Regions of Interest (ROI), using FRT to RONI, and converted into blocks; afterward, Hessenberg decomposition is applied on blocks selected by means of fuzzy logic. The binary watermark is quantized into the medical image. It has made the watermarking system non-fragil and secure.The proposed scheme's performance is evaluated on various attack situations, including noise and scale assaults. Watermarked pictures with enhanced PSNR ranging from 56.9902 to 57.2356 have been produced using the proposed technique. In particular, we achieved high NC values of 0.9801 on applying image processing attacks. A series of tests are performed on the system to determine its sensitivity, and the results are compared to protocols. The simulation results demonstrate that the suggested solution offers a high level of security while being imperceptible and robust to various attacks.

Robust Medical Image Watermarking Scheme Using PSO, LWT, and Hessenberg Decomposition

“…The findings indicate that the envisaged system exhibited satisfactory performance, even in the presence of a filtering attack. Furthermore, upon conducting a comparative analysis of the outcomes with other extant methodologies, namely [28,[30][31][32], at intensities of 0.05, 0.025, and 0.10 (as presented in Table 5), it was noted that the findings of [31,32] bear similarity to the suggested approach at an intensity of 0.050. At the intensity of 0.025 and 0.010, the performance of our approach is much better than other similar schemes.…”

“…The obtained results demonstrate that, when exposed to a JPEG compression attack, the proposed approach performed better than expected. Furthermore, upon conducting a comparative analysis of the outcomes with other pre-existing methodologies, namely [28,[30][31][32], concerning quality factors 80 and 90 (as presented in Table 3), it was observed that the outcomes of [28,32] exhibit a resemblance to the proposed scheme at a quality factor of 90. At a quality factor of 80, it can be observed that only the reference [32] is in closer proximity to the subject matter being discussed.…”

“…The findings indicate that the envisaged system exhibited satisfactory performance even in the presence of a noise attack. In addition, a comparative analysis was performed to evaluate the results of the proposed methodology in relation to other established approaches, specifically [28,[30][31][32], with respect to varying noise intensity levels of 0.001, 0.005, 0.010, and 0.020 (as presented in Table 7). The findings indicate that the outcomes of [28,30] bear a resemblance to the proposed scheme at intensity levels of 0.001 and 0.020.…”

“…In addition, a comparative analysis was performed to evaluate the results of the proposed methodology in relation to other established approaches, specifically [28,[30][31][32], with respect to varying noise intensity levels of 0.001, 0.005, 0.010, and 0.020 (as presented in Table 7). The findings indicate that the outcomes of [28,30] bear a resemblance to the proposed scheme at intensity levels of 0.001 and 0.020. At an intensity level of 0.010, it is apparent that [30][31][32] exhibit superior performance.…”

“…Moreover, the transform domain is often used to improve the imperceptibility of a watermarking scheme. There are several reports available in the literature, such as [17,18,[20][21][22][23][24][25][26][27][28][29][30][31][32].…”