Title :
Neural networks for computed tomography
Author :
Morisue, M. ; Sakai, K. ; Koinuma, H.
Author_Institution :
Dept. of Electron. Eng., Saitama Univ., Urawa, Japan
Abstract :
Proposes a novel application of neural networks to cognitive tasks of computed tomography (CT). The principle of Hopfield type neural networks to reconstruct the image from the projected densities is described. The technique of reconstruction is based on the algebraic reconstruction technique (ART). Simulation results for a model of an image, where the reconstruction space was divided into 32×32 elements with 9 color degrees, show the satisfactory performance in terms of accuracy and computation time by application of the neural networks
Keywords :
Hopfield neural nets; computerised tomography; image reconstruction; medical image processing; Hopfield type neural networks; algebraic reconstruction technique; cognitive tasks; computation time; computed tomography; medical image processing; projected densities; reconstruction space; Computational modeling; Computed tomography; Computer networks; Concurrent computing; Equations; Hopfield neural networks; Image reconstruction; Neural networks; Subspace constraints; X-ray imaging;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.230646
