Radiology Medical Image Fusion Using Neural Network

Abstract

Image fusion plays a key role in combining data from many sources into a single, more intelligible result in a range of clinical applications.The use of a medical image fusion technology can be beneficial to aid the physician in performing combination Preoperative preparation, intra-operative supervision, and interventional treatment are all part of the diagnostic process.In this thesis, proposed a technique with a combined model of PCA and CNN for the fusion of images A real-time image fusion method that uses pre-trained neural networks to create one image using features from several sources in real-time.Based on deep neural network feature maps, using a convolutional network a unique technique is produced to merge the images.Due to the vast number of capture systems, picture fusion has become increasingly important in current image processing applications.Fusion of pictures is used to combine multi-temporal, multi-view, and inter-data into single imaging with improved image quality and important feature integrity retained It's a crucial stage in a range of applications, including human-robot interaction, aircraft, satellites, and medical imaging, as well as robotics and object tracking.