Defining optimal feature sets for segmentation by statistical pattern recognition

James M. Coggins; Changhua Huang

doi:10.1117/12.146614

23 June 1993 Defining optimal feature sets for segmentation by statistical pattern recognition

James M. Coggins, Changhua Huang

Proceedings Volume 2035, Mathematical Methods in Medical Imaging II; (1993) https://doi.org/10.1117/12.146614
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

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.

Citation Download Citation

James M. Coggins and Changhua Huang "Defining optimal feature sets for segmentation by statistical pattern recognition", Proc. SPIE 2035, Mathematical Methods in Medical Imaging II, (23 June 1993); https://doi.org/10.1117/12.146614

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