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Proceedings Paper

Neural network image compression using Gabor primitives
Author(s): Mary P. Anderson; David G. Brown; Alexander C. Schneider
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Paper Abstract

A back propagation neural network was used to compress simulated nuclear medicine liver images with and without simulated lesions. The network operated on the Gabor representation of the image, in order to take advantage of the apparent similarity between that representation and the natural image processing of the human visual system. The quality of the compression scheme was assessed objectively by comparing the original images to the compressed/reconstructed images through calculation of an index shown to track with human observers for this class of image, the Hotelling trace. Task performance was measured pre- and post-compression for the task of classifying normal versus abnormal livers. Compression of even 2:1 was found to result in significant performance degradation in comparison with other means of compression, but produced a visually pleasing image.

Paper Details

Date Published: 1 June 1992
PDF: 7 pages
Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); doi: 10.1117/12.59441
Show Author Affiliations
Mary P. Anderson, Ctr. for Devices and Radiological Health/FDA (United States)
David G. Brown, Ctr. for Devices and Radiological Health/FDA (United States)
Alexander C. Schneider, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 1652:
Medical Imaging VI: Image Processing
Murray H. Loew, Editor(s)

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