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Proceedings Paper

Medical image enhancement using resolution synthesis
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Paper Abstract

We introduce a post-processing approach to improve the quality of CT reconstructed images. The scheme is adapted from the resolution-synthesis (RS)1 interpolation algorithm. In this approach, we consider the input image, scanned at a particular dose level, as a degraded version of a high quality image scanned at a high dose level. Image enhancement is achieved by predicting the high quality image by classification based linear regression. To improve the robustness of our scheme, we also apply the minimum description length principle to determine the optimal number of predictors to use in the scheme, and the ridge regression to regularize the design of the predictors. Experimental results show that our scheme is effective in reducing the noise in images reconstructed from filtered back projection without significant loss of image details. Alternatively, our scheme can also be applied to reduce dose while maintaining image quality at an acceptable level.

Paper Details

Date Published: 7 February 2011
PDF: 6 pages
Proc. SPIE 7873, Computational Imaging IX, 787307 (7 February 2011); doi: 10.1117/12.882878
Show Author Affiliations
Tak-Shing Wong, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)
Jean-Baptiste Thibault, GE Healthcare (United States)
Ken D. Sauer, Univ. of Notre Dame (United States)

Published in SPIE Proceedings Vol. 7873:
Computational Imaging IX
Charles A. Bouman; Ilya Pollak; Patrick J. Wolfe, Editor(s)

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