MULTI MODAL MEDICAL IMAGE FUSION USING NON-SUB SAMPLED ROTATED COMPLEX WAVELET TRANSFORM

The multimodal medical image fusion technique is a current research issue in the fields of medical imaging and radiation medicine, and it is generally known in the medical and engineering fields. A multimodality medical image fusion technique is extremely important in biomedical research and disease analysis. The process of combining relevant information from two or more images into a single image is known as image fusion. In this project, we use images from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) of the same patient to better visualise tumours using feature based Fusion of CT and MRI. This paper presents a unique approach to Multimodality Medical Image Fusion (MMIF), a technique for combining CT and MRI data from the same patient into a new slice using a Non-sub sampled Rotated Complex Wavelet Transform (NSRCxWT) and Discrete Wavelet Transform (DWT). The forward NSRCxWT and DWT are applied separately to CT and MRI images to extract complementary and edge related features. Using Average and Maximum value selection fusion rules, these features are then combined to form a composite spectral plane. The inverse transformation is then applied, resulting in a new, visually enhanced, and enriched fused image. The objective and subjective evaluation metrics are used to evaluate the quality of these fused images