Energy- and Contrast-Aware NSST-Based Medical Image Fusion with Anisotropic Diffusion Enhancement
Mohammed Rafiq, Prabhishek Singh, Ankur Maurya, Manoj DiwakarIntroduction:
Medical image fusion (MIF) integrates complementary information from multiple modalities to enhance diagnostic accuracy. However, existing approaches often struggle to suppress noise while preserving critical anatomical details.
Materials and Methods:
We propose ENCAF-NSST-AD, a fusion technique that combines the Non-Subsampled Shearlet Transform (NSST) with anisotropic diffusion (AD) refinement. Lowfrequency base layers (LFBL) are fused using an energy-based weighting strategy, while high-frequency detail layers (HFDL) are integrated through a contrast-aware rule. The reconstructed image is further enhanced using AD filtering with optimized parameters to improve clarity and preserve structural information.
results:
Experiments on standard medical image datasets demonstrate that ENCAF-NSST-AD consistently outperforms existing methods in both visual quality and quantitative metrics, including FMI, FF, entropy, and standard deviation.
Results:
Experiments on benchmark datasets demonstrate that ENCAF-NSST-AD achieves superior fusion quality compared with existing state-of-the-art methods, exhibiting enhanced visual clarity and higher values across standard quantitative metrics.
Discussion:
The proposed method effectively balances noise suppression with edge preservation, addressing limitations of traditional MIF schemes. By combining adaptive fusion rules with edgepreserving diffusion, ENCAF-NSST-AD preserves global contrast and fine anatomical boundaries, resulting in fused images more suitable for clinical interpretation. Its energy- and contrast- aware fusion with AD refinement represents a clinically relevant and patent-oriented advancement.
Conclusion:
The integration of NSST with adaptive fusion rules and AD refinement produces robust and diagnostically meaningful fusion outcomes. ENCAF-NSST-AD is an effective MIF approach that can enhance clinical decision support in medical imaging.