Privacy Preservation of Medical Images Using Hybrid Chaotic Maps

In recent times, the utilization of medical images in telemedicine applications has been experiencing significant growth in healthcare sector. After the COVID-19 pandemic, many online consultations are growing, which is convenient for old age people and time-saving compared to offline consultations. The hospitals send the patient medical reports online. The medical image determines the physical conditions of the patient. There is a chance that intruders misuse medical data for fake insurance claims. Modifying the medical report for intentional purposes undergoes false medical diagnosis. Most security breaches have occurred during the transmission of medical records. The main objective is to use a hybrid chaotic map combining Henon maps and logistic maps to improve the security of medical images. A pseudo-code number generator is utilized for secure key generation to encrypt and decrypt the image using Mersenne Twister. This proposed work has proven to conflict with statistical and differential attacks. The hybrid chaotic maps are implemented using online open-source medical image datasets, getting better results than the existing scheme. Performance of this scheme is measured using various metrics, such as histogram analysis, correlation coefficients, avalanche effect, NPCR, and UACI, to determine its effectiveness. This proposed work provides enhanced security while transmitting medical data in telemedicine applications.