A neural network optimization-based method of image reconstruction from projections

Authors:
Robert Cierniak
Affiliations:
Technical University of Czestochowa, Departament of Computer Engineering, Czestochowa, Poland
Venue:
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Year:
2010

Citing 6
Cited 1

Fundamentals of digital image processing

Fundamentals of digital image processing
Digital image processing: concepts, algorithms and scientific applications

Digital image processing: concepts, algorithms and scientific applications
Applied Neural Networks For Signal Processing

Applied Neural Networks For Signal Processing
A 2D approach to tomographic image reconstruction using a Hopfield-type neural network

Artificial Intelligence in Medicine
New neural network algorithm for image reconstruction from fan-beam projections

Neurocomputing
Vector-entropy optimization-based neural-network approach to image reconstruction from projections

IEEE Transactions on Neural Networks

A neuronal approach to the statistical image reconstruction from projections problem

ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

The image reconstruction from projections problem remains the primary concern for scientists in area of computer tomography. The presented paper describes a new approach to the reconstruction problem using a recurrent neural network. The reconstruction problem is reformulated to the optimization problem. The optimization process is performed during the minimizing of the energy functions in the net determined by the different error measures. Experimental results show that the appropriately designed neural network is able to reconstruct an image with better quality than obtained from conventional algorithms.