الفهرس | Only 14 pages are availabe for public view |
Abstract The advancement of medical imaging has led to the acquisition of image data from multiple modalities, necessitating the development of robust algorithms for accurate and reliable fusion of such diverse image sets. Medical image fusion plays a crucial role in enhancing the clinical applicability of medical images by combining information from different modalities into a single fused image that provides comprehensive and instructive insights. In recent years, significant efforts have been devoted to expanding the repertoire of image fusion algorithms, particularly in the absence of standardized benchmarks and comprehensive code libraries that can support state-of-the-art techniques. This thesis presents different proposed Deep Learning (DL) image fusion algorithms in medical imaging applications, ultimately contributing to improved healthcare diagnostics. Our findings highlight the superior performance of specific DL techniques, for fusing different medical image modalities and achieving excellent restoration quality. The First Proposal presents a comprehensive performance analysis of different medical image fusion techniques applied to a wide range of medical images. The Second Proposal describes a medical image fusion technique based on Relative Total Variation Decomposition (RTVD) that can concurrently maintain the texture and contrast information of the input images. |