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Proceedings Paper

Intelligent data elimination for a rare event application
Author(s): Ramkumar Narayanswamy; John L. Metz; Kristina M. Johnson
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Paper Abstract

Rare event applications are characterized by the event-of- interest being hidden in a large volume of routine data. The key to success in such situations is the development of a cascade of data elimination strategies, such that each stage enriches the probability of finding the event amidst the data retained for further processing. Automated detection of aberrant cells in cervical smear slides is an example of a rare event problem. Each slide can amount to 2.5 gigabytes of raw data and only 1 in 20 slides are abnormal. In this paper we examine the use of template matching, artificial neural networks, integrated optical density and morphological processing as algorithms for the first data elimination stage. Based on the experience gained, we develop a successful strategy with improves the overall event probability in the retained data from 0.01 initially to 0.87 after the second stage of processing.

Paper Details

Date Published: 1 October 1998
PDF: 12 pages
Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); doi: 10.1117/12.323161
Show Author Affiliations
Ramkumar Narayanswamy, Aztek Engineering (United States)
John L. Metz, Univ. of Colorado/Boulder (United States)
Kristina M. Johnson, Univ. of Colorado/Boulder (United States)


Published in SPIE Proceedings Vol. 3460:
Applications of Digital Image Processing XXI
Andrew G. Tescher, Editor(s)

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