A robust blind medical image watermarking approach for telemedicine applications

A robust and secured fusion based hybrid medical image watermarking approach using RDWT-DWT-MSVD with Hyperchaotic system-Fibonacci Q Matrix encryption

Digital image watermarking, the process of marking a host image with a watermark, is generally used to authenticate the data. In the medical field, it is of utmost importance to verify the authenticity of the data using Medical Image Watermarking (MIW), especially in e-healthcare applications. Recently, MIW with image fusion, the merging of multimodal images to improve image quality, is being widely utilized to make diagnosis more accessible and precise with the verified data. This paper offers a durable and secure fusion-based hybrid MIW approach. The method initially used Fast Filtering (FF) to merge two medical images from different modalities to form the cover image. A first-level Redundant Discrete Wavelet Transform (RDWT) is employed on this host image to locate the component with the highest entropy. Then a single-level Discrete Wavelet Transform (DWT) is applied to it. It performed a Multi-resolution Singular Value Decomposition (MSVD) on the wavelet decomposed component and the embedding watermark. Finally, a Hyperchaotic System-Fibonacci Q Matrix (HFQM) encryption system was utilized, which increases the watermarked image's security. Here, using various medical images, the performance of the proposed technique is evaluated. Without any attacks, the approach achieved a maximum Peak Signal to Noise Ratio (PSNR) of 90.31 dB and a Structural Similarity Index Matrix (SSIM) of value 1. Various watermarking assaults were employed to test the proposed method's resilience. The suggested technique achieved a perfect value of 1 for the Normalised Correlation (NC) for almost all attacks with acceptable imperceptibility, which substantially improves over current procedures. The suggested technique's average embedding and extraction times are 0.3958 and 0.4721 seconds, respectively, which are pretty short compared to existing approaches.

Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map

In this pandemic situation, radiological images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyzed and filtered themselves. Due to an overflow of patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with integrity and availability. Chaos-based cryptography has proven a useful technique in the process of medical image encryption. The specialty of using chaotic maps in image security is its capability to increase the unpredictability and this causes the encryption robust. There are large number of literature available with chaotic map; however, most of these are not useful in low-precision devices due to their time-consuming nature. Taking into consideration of all these facts, a modified encryption technique is proposed for 2D COVID-19 images without compromising security. The novelty of the encryption procedure lies in the proposed design which is split into mainly three parts. In the first part, a variable length gray level code is used to generate the secret key to confuse the intruder and subsequently it is used as the initial parameter of both the chaotic maps. In the second part, one-stage image pixels are shuffled using the address code obtained from the sorting transformation of the first logistic map. In the final stage, a complete diffusion is applied for the whole image using the second chaotic map to counter differential and statistical attack. Algorithm validation is done by experimentation with visual image and COVID-19 X-ray images. In addition, a quantitative analysis is carried out to ensure a negligible data loss between the original and the decrypted image. The strength of the proposed method is tested by calculating the various security parameters like correlation coefficient, NPCR, UACI, and key sensitivity. Comparison analysis shows the effectiveness for the proposed method. Implementation statistics shows time efficiency and proves more security with better unpredictability.

Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models

Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8-10% of better system performance than other existing techniques.