An Optimized Edge Preserving Medical Image Fusion Framework Using NSCT for Improved Diagnostic Accuracy
Keywords:
Medical image fusion, Contrast Limited Adaptive Histogram Equalization, Non-Subsampled Contourlet Transform, visual quality, medical diagnosis and treatment planningAbstract
Medical image fusion plays a crucial role in enhancing diagnostic accuracy by integrating complementary information from multiple imaging modalities. This study presents an optimized fusion framework that combines Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement, Non-Subsampled Contourlet Transform (NSCT) for multi-scale decomposition, guided filtering for edge preservation, and gradient-based weight computation for detail layer fusion. The proposed method effectively preserves critical structural details while minimizing information loss and fusion artifacts. Experimental results demonstrate that the method achieves a minimum improvement of 5.75% and a maximum improvement of 38.23% in information retention, while fusion loss is reduced by 6.09% to 79.52%, and fusion artifacts are minimized by 10.00% to 96.97% compared to traditional approaches. The fused images exhibit superior visual quality, enhanced contrast, and improved feature preservation, making the method highly suitable for medical diagnosis and treatment planning
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