Medical image transmission across cloud-enabled healthcare networks has become increasingly common in modern telemedicine systems. However, existing approaches suffer from increased computational overhead, inadequate resistance against statistical attacks, poor reconstruction quality and so on. Hence, an efficient secure medical image transmission framework that integrates compression and encryption is needed. This work presents a ‘denoising-compression-encryption’ framework, which integrates Huang’s Block Truncation Coding (HBTC) with Gauss-iterated map with Inertia Weight-based Chimp Optimization Algorithm (GIW-CHO) optimized Elliptic Curve Cryptography (ECC) and termed as ‘OKBECC’. Initially, the images are denoised for intensity stabilization and then HBTC is employed to binarize and decorrelate the images. Finally, encryption is performed by ECC using the keys optimized by GIW-CHO algorithm. HBTC greatly influences the interpixel-correlation and histogram structure by reducing the entropy to approximately one before encryption, which makes it unpredictable. GIW-CHO helps explore larger key space and provides stronger optimal keys, which helps in attaining better quality of reconstructed image. The proposed work is tested across different image modalities including X-ray, ultrasound, Magnetic Resonant Imaging (MRI), Computed Tomography (CT) and histopathology upon standard performance measures. The proposed OKBECC shows an average NPCR, UACI and cipher entropy of about 99.6%, 33.4% and 7.99 bits, respectively, which outperforms multiple existing algorithms. In addition, the image reconstruction quality is proven with PSNR of about 68 dBs, which is clinically acceptable.
Selvaraj et al. (Fri,) studied this question.