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Proceedings Paper

Neural network decision functions for a limited-view reconstruction task
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Paper Abstract

Neural networks are applied to a Rayleigh discrimination task for simulated limited-view computed tomography with maximum-entropy reconstruction. Network performance is compared to that obtained using the best machine approximation to the ideal observer found in an earlier investigation. Results obtained on 2D subimage inputs are compared with those for 1D inputs and presented previously at this conference. Back-propagation neural networks significantly outperform the `best' standard nonadaptive linear machine observer and also the intuitively appealing `matched filter' obtained by averaging over the images in a large training data set. In addition, the back-propagation neural network operating on 2D subimages performs significantly better than that limited to 1D inputs. Finally, improved performance on this Rayleigh task is found for nonlinear (over linear, that is, simple perceptron) neural network decision strategies.

Paper Details

Date Published: 24 June 1998
PDF: 8 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310912
Show Author Affiliations
David G. Brown, FDA Ctr. for Devices and Radiological Health (United States)
Mary S. Pastel, FDA Ctr. for Devices and Radiological Health (United States)
Kyle J. Myers, FDA Ctr. for Devices and Radiological Health (United States)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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