Abstract
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<0.01), reflecting better signal fidelity and structural preservation; vs. CLAHE, QIE PSNR was -3.4 dB lower on CT and -3.3 dB lower on MR (both P<0.001), but SSIM differed by <0.001 (P≥0.13). CNR with QIE (CT: 4.00±3.54; MR: 3.66±2.81) was not statistically different from any method (P≥0.05). CONCLUSIONS: The proposed QIE algorithm demonstrates superior performance in enhancing the contrast and preserving the structural details of medical images. By leveraging quantum-inspired mechanisms, the algorithm shows potential for improving diagnostic accuracy and supporting clinical treatment planning. Future work will explore the application of this algorithm to other imaging modalities, investigate its effectiveness as a preprocessing step for commercial artificial intelligence (AI) models, and study the integration with actual quantum computing platforms.