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Proceedings Paper

Optimization for Object Localization of the Constrained Algebraic Reconstruction Technique
Author(s): K. M. Hanson
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Paper Abstract

A method for optimizing image-recovery algorithms is presented that is based on how well the specified task of object localization can be performed using the reconstructed images. The task performance is numerically assessed by a Monte Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, image recovery, and performance of the stated task based on the final image. This method is used to optimize the constrained Algebraic Reconstruction Technique (ART), which reconstructs images from their projections under a nonnegativity constraint by means of an iterative updating procedure. The optimization is performed by finding the the relaxation factor, which is employed in the updating procedure, that yields the minimum rms error in estimating the position of discs in the reconstructed images. It is found that the optimum operating points for the best object localization are essentially the same as those obtained earlier when the performance of simple object detection is to be optimized.

Paper Details

Date Published: 1 May 1989
PDF: 8 pages
Proc. SPIE 1090, Medical Imaging III: Image Formation, (1 May 1989); doi: 10.1117/12.953199
Show Author Affiliations
K. M. Hanson, Los Alamos National Laboratory (United States)


Published in SPIE Proceedings Vol. 1090:
Medical Imaging III: Image Formation
Samuel J. Dwyer; R. Gilbert Jost; Roger H. Schneider, Editor(s)

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