Open AccessDeep learning approach for fusion of magnetic resonance imaging-positron emission tomography image based on extract image features using pretrained network (VGG19)
Author(s) -
Nasrin Amini,
Ahmad Mostaar
Publication year - 2022
Publication title -
journal of medical signals and sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.337
H-Index - 21
ISSN - 2228-7477
DOI - 10.4103/jmss.jmss_80_20
Subject(s) - artificial intelligence , positron emission tomography , image fusion , convolutional neural network , computer science , entropy (arrow of time) , magnetic resonance imaging , pattern recognition (psychology) , computer vision , cross entropy , mutual information , hue , deep learning , sørensen–dice coefficient , image (mathematics) , nuclear medicine , image segmentation , physics , medicine , radiology , quantum mechanics
The fusion of images is an interesting way to display the information of some different images in one image together. In this paper, we present a deep learning network approach for fusion of magnetic resonance imaging (MRI) and positron emission tomography (PET) images.