A novel approach for contrast enhancement in medical images based on quantum-inspired enhancement algorithm

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are essential in clinical diagnosis and treatment planning, but their images are often compromised by limited contrast and insufficient detail, reducing diagnostic clarity. Traditional enhancement methods—such as histogram equalization (HE) can improve visibility but may introduce noise, over-enhancement, or structural distortion. Quantum-inspired computational techniques have recently emerged as promising tools for nonlinear and adaptive image processing. Building on the quantum signal processing (QSP) framework, this study proposes a quantum-inspired enhancement (QIE) algorithm designed to improve medical image contrast while preserving structural details. Methods: We propose a QIE algorithm that embeds a three-pixel quantum-correlation system within a QSP framework. After normalizing grayscale values, each 3×3 neighborhood is mapped to superposition states; edge-sensitive basis states are selectively accumulated in four orientations to produce the enhanced output. The algorithm was evaluated using T2-weighted magnetic resonance (MR) brain images and CT lung images obtained from 10 different patients. Its performance was compared with four representative classical enhancement methods: HE, contrast-limited adaptive HE (CLAHE), fuzzy HE (FHE), and wavelet-based enhancement (WBE), employing quantitative metrics such as entropy, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and contrast-to-noise ratio (CNR). Paired two-sided t-tests (α=0.05) were used. Results: QIE reached the highest mean entropy on both datasets (CT: 4.37±0.31; MR: 6.45±0.16) vs. HE 4.00±0.25 (P=2.8×10−4) and 5.67±0.16 (P=2.3×10−7) respectively, indicating superior information retention and detail enhancement. Its PSNR and SSIM were significantly better than HE, FHE, and WBE (all P