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Proceedings Paper

Compression and reconstruction of medical image sequences
Author(s): James B. Farison; Youngin O. Shin; Mark E. Shields
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Paper Abstract

Many important imaging applications generate a sequence of images that are (or can be made to be) a spatially invariant image sequence with linearly additive contributions from the components that form the images. They include functional images in nuclear medicine, multiparameter MR imaging, multi-energy x-ray imaging for DR and CT, and multispectral satellite images. Recent results in the modelling and analysis of linearly additive spatially invariant image sequences are based on the inherent structure of such images, and can be used to achieve significant data compression for image storage and still provide good reconstruction. The technique is applied here to a human renogram, with compression of a very noisy 180-image sequence to a 4-image set. The resulting reconstruction illustrates the potential of the method.

Paper Details

Date Published: 9 December 1992
PDF: 11 pages
Proc. SPIE 1768, Mathematical Methods in Medical Imaging, (9 December 1992); doi: 10.1117/12.130910
Show Author Affiliations
James B. Farison, Univ. of Toledo (United States)
Youngin O. Shin, Univ. of Toledo (United States)
Mark E. Shields, Univ. of Toledo (United States)

Published in SPIE Proceedings Vol. 1768:
Mathematical Methods in Medical Imaging
David C. Wilson; Joseph N. Wilson, Editor(s)

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