An Investigation on Multimodal Brain Image Fusion in the Time-Frequency Domain using Wavelet Transforms

Image fusion is a process of merging or combining two or more images into a single image. This process helps to provide more information by retaining salient information from the source images. In recent years, image fusion research has extended promptly as imaging technologies are advanced. The rapid advancement of imaging modalities, particularly in the medical field, has resulted in the creation of an increasing number of fusion algorithms. Wavelets are the most applied transforms as they provide spatial and spectral representations. The performance of each wavelet family differs in the fusion with respect to its characteristics. To determine the best family of wavelet-based fusion algorithms, it is suggested to use an analytical technique to compare various wavelets used in brain image fusion. In this study, Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) images are used to create fused images. In which, Magnetic Resonance (MR) images have higher spatial resolution and better soft tissue characterization, CT images emphasize three-dimensional imaging and have minimal scan times and high imaging resolutions. It is proposed to find the best-performing wavelet filter bank to support hybrid fusion framework. A total of 87 CT and MR images are collected from different sources, and wavelet transform is applied to them to fuse. The performance of each wavelet family is evaluated on the basis of the Standard Deviation (SD) and Entropy (Epy).