NDLI: Dynamic image reconstruction and synthesis framework using deep learning algorithm

Content Provider	IET Digital Library
Author	Jeyaraj, Pandia Rajan Nadar, Edward Rajan Samuel
Abstract	This research study shows an effective deformable complex 3D image reconstruction and image synthesis technique by consolidating needed high-level features from a deep convolutional neural network (CNN) system. By recognising the inherent deep features in image patches lead to information discovery in medicinal imaging. Utilising the ADNI and LONI imaging datasets, the performance of the proposed deep learning algorithm image reconstruction and synthesis performance was verified. For validation, various performance indices obtained with the proposed deep learning algorithm were compared with two conventional algorithms namely support vector machine and CNN. Likewise, to reveal the adaptability of the proposed image reconstruction and synthesis system, synthesis and reconstruction experiments were directed on the 7 T cerebrum magnetic resonance image. As presented in the study outcomes, the proposed method can accomplish predominant performance compared with other cutting-edge techniques with either low- or high-level features in terms of the synthesis and reconstruction rate. The proposed algorithm has a training time of 5 s with a structural similarity index of 0.97. In all investigations, the outcome shows that the proposed image reconstruction framework reliably exhibited progressively precise outcomes when contrasted with best in class. Hence, it can be used for possible precise image reconstruction and synthesis related applications.
Starting Page	1219
Ending Page	1226
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 7, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0900
Journal	IET Image Processing
Publisher Date	2020-02-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	ADNI Imaging Dataset Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Biophysics of Neurophysiological Processes Brain Cerebrum Magnetic Resonance Image Computer Vision And Image Processing Technique Deep Convolutional Neural Network System Deep Learning Algorithm Image Reconstruction High-level Feature Image Classification Image Patches Image Recognition Image Reconstruction Image Reconstruction Framework Image Synthesis Technique Knowledge Engineering Technique Learning in AI LONI Imaging Dataset Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Medicinal Imaging Neural Nets Patient Diagnostic Method And Instrumentation Reconstruction Experiments Reconstruction Rate Spectroscopy Support Vector Machine Synthesis Framework Synthesis Performance Synthesis System
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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