Neural network image compression using Gabor primitives

Mary P. Anderson; David G. Brown; Alexander C. Schneider

doi:10.1117/12.59441

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1 June 1992 Neural network image compression using Gabor primitives

Mary P. Anderson; David G. Brown; Alexander C. Schneider

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Proceedings Volume 1652, Medical Imaging VI: Image Processing; (1992) https://doi.org/10.1117/12.59441
Event: Medical Imaging VI, 1992, Newport Beach, CA, United States

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.

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Mary P. Anderson, David G. Brown, and Alexander C. Schneider "Neural network image compression using Gabor primitives", Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); https://doi.org/10.1117/12.59441

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