A 2D approach to tomographic image reconstruction using a Hopfield-type neural network
Artificial Intelligence in Medicine
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
A New Approach to Image Reconstruction from Projections Using a Recurrent Neural Network
International Journal of Applied Mathematics and Computer Science - Special Section: Selected Topics in Biological Cybernetics, Special Editors: Andrzej Kasiński and Filip Ponulak
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
A neural network optimization-based method of image reconstruction from projections
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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
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In this paper we propose a multiobjective decision making based neural-network model and algorithm for image reconstruction from projections. This model combines the Hopfield's model and multiobjective decision making approach. We develop a weighted sum optimization based neural-network algorithm. The dynamical process of the net is based on minimization of a weighted sum energy function and Euler's iteration, and apply this algorithm to image reconstruction from computer-generated noisy projections and Siemens Somatson DR scanner data, respectively. Reconstructions based on this method is shown to be superior to conventional iterative reconstruction algorithms such as the multiplicate algebraic reconstruction technique (MART) and convolution from the point of view of accuracy of reconstruction. Computer simulation using the multiobjective method shows a significant improvement in image quality and convergence behavior over the conventional algorithms