A New Approach for Image Security Enhancement Using Ternary Logic Linear Feedback Shift Register for Cryptographic Applications

[Purpose] This paper addresses a core decision problem in healthcare data governance: how should healthcare decision-makers optimally select encryption parameters under resource and threat-model constraints? To answer this, a formal multi-criteria decision framework is developed and instantiated through a novel ternary linear feedback shift register (LFSR)-based encryption system, providing clinicians and security engineers with principled, quantitative parameter-selection guidance for lightweight image-encryption deployment on resource-constrained medical devices. [Design/Methodology/Approach] The proposed method extends traditional binary LFSRs to the ternary domain GF(3), operating over three logic states {0, 1, 2}, to generate pseudo-random keystreams that drive a pixel-permutation cypher. The system was evaluated on representative images across three clinically distinct modalities: kidney ultrasound, brain MRI, and multiple sclerosis (MS) MRI. Evaluation used standard security metrics including NPCR, UACI, information entropy, MSE, PSNR, SSIM, and pixel-correlation coefficients. [Findings] The model achieves strong security performance with NPCR of 98.04%, entropy of 6.80 bits (kidney ultrasound), and UACI of 27.96% (brain MRI). Encrypted images show near-uniform histograms and near-zero pixel correlations (≤ 0.022). Correct-key decryption recovers originals with high fidelity (SSIM 0.9903–1.0000, PSNR 44.52–52.21 dB), while incorrect keys produce unintelligible output. [Originality/Value] This work contributes a formally grounded decision framework for encryption parameter selection under constraints and introduces the first deployment of ternary (GF(3)) LFSR-based cipher for medical image protection, expanding the key space while maintaining low computational complexity. [Implications] The framework supports risk-based governance and regulatory compliance in healthcare, enabling practical deployment on embedded and IoT medical devices.