Wavelet Transform Based Image Fusion for Brain Tumour Detection

Wavelet transform-based image fusion is a powerful technique used in medical imaging to enhance the visualization of brain tumors by combining multiple imaging modalities. Brain tumor detection requires high precision, as different imaging techniques, such as MRI (Magnetic Resonance Imaging) and CT (Computed Tomography), provide complementary information. MRI offers excellent soft tissue contrast, while CT scans provide detailed bone structures and tumor localization. However, analyzing these images separately may lead to loss of critical diagnostic details. In the designed project, wavelet transform is utilized to fuse images from different modalities, preserving essential features while eliminating redundant information. The wavelet decomposition process helps in breaking down images into multiple frequency components, allowing selective enhancement of edges, textures, and significant details. The fused image retains high spatial resolution and improved contrast, aiding radiologists in accurate tumor identification and classification. The method ensures that diagnostic relevant features are highlighted while reducing noise and artifacts. The proposed wavelet-based fusion method provides an efficient solution for medical image analysis by enhancing critical tumor characteristics that might be missed in individual scans. Future advancements may include deep learning integration for automated tumor segmentation and classification, further improving diagnostic efficiency.