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Proceedings Paper

Defining optimal feature sets for segmentation by statistical pattern recognition
Author(s): James M. Coggins; Changhua Huang
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Paper Abstract

A methodology for task-sensitive pixel classification is defined based on multiscale Gaussian derivatives and statistical pattern recognition methods. Multiscale Gaussian derivatives are approximated by Gaussian and offset-Gaussian filters to decrease computational requirements. A method is devised for computing a discriminant vector between classes based on class isolation and compactness. The optimal discriminant vector is converted back into image form and applied to the image to determine whether a 1-D feature space is adequate to separate the classes.

Paper Details

Date Published: 23 June 1993
PDF: 9 pages
Proc. SPIE 2035, Mathematical Methods in Medical Imaging II, (23 June 1993); doi: 10.1117/12.146614
Show Author Affiliations
James M. Coggins, Univ. of North Carolina/Chapel Hill (United States)
Changhua Huang, Univ. of North Carolina/Chapel Hill (United States)

Published in SPIE Proceedings Vol. 2035:
Mathematical Methods in Medical Imaging II
Joseph N. Wilson; David C. Wilson, Editor(s)

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