Medical Image Fusion Enhancement through Curvelet Transform

Medical image fusion, combining data from multiple imaging modalities, plays a crucial role in improving diagnostic accuracy, treatment planning, and medical research. The Curvelet transform, renowned for its ability to efficiently capture directional and multiscale information, emerges as a powerful tool for this task. This work provides a comprehensive overview of Curvelet transform applications in medical image fusion. We delve into the fundamental principles of the Curvelet transform, highlighting its advantages in the context of medical imaging. Furthermore, we explore various fusion techniques utilizing the Curvelet transform and showcase its successful implementation in fusing images from modalities like MRI, CT, PET, and SPECT. Finally, we identify current challenges and promising avenues for future research in this domain. The Curvelet transform's unique capabilities in representing multiscale, directional, and sparse data provide a robust foundation for advancements in medical image analysis and processing, including image enhancement, feature extraction, registration, segmentation, and, most significantly, image fusion.