Fusion of CT and MR Images with Multi-criteria Integration

Image fusion represents a sophisticated technique that integrates CT and MR images into a unified composite image through the application of multiple fusion criteria. This advanced methodology significantly enhances image quality and information density, making it particularly valuable for widespread medical applications. The fusion process typically involves algorithms such as wavelet transform, principal component analysis (PCA), or deep learning-based approaches, where key functions like coefficient selection and spatial frequency integration play crucial roles. Through systematic evaluation of fused images - employing metrics like peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information - quantitative analysis and comparative assessment of fusion effectiveness can be achieved. The evolution of image fusion technology continues to expand possibilities in medical diagnosis and research, providing physicians and researchers with more comprehensive and precise imaging information for enhanced clinical decision-making.