IMAGE FUSION WITH CT AND MRI IMAGES FOR IMPROVING QUALITY OF DIAGNOSTICS

Abstract

-Fusing several medical images will enhance sickness diagnosis and demonstrate the complicated relationship between them for medical research. Existing techniques are time consuming and require a higher amount of data to train the models.We will obtain complex information from medical pictures such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) in this model by fusing them using multi-stage fusion networks. We will utilise the Dual Tree Complex Wavelet Transform (DTCWT) to extract the challenging and related information from each image, and then divide the segmentation to get the segmented picture. The suggested technique uses the Dual Tree Complex Wavelet Transform to integrate several sample medical images, with the source medical image being converted to grayscale and degraded before the wavelet coefficients are produced using the DTCWT.The fused coefficients are then produced using wavelet approximation. The Inverse Dual Tree Complex Wavelet Transform is employed to develop the final fused picture. Segmentation is often used to create a segmented picture for better visual representation. The suggested technology has the potential to provide a higher quality final fused image.