Application of statistical theory to edge extraction in medical images

Paul Chan, Lan Li, Robert L. Lytton, Ronald A. Karwoski, Richard A. Robb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The authors describe an edge extraction algorithm specially developed for noisy images. It is based on the statistical theory of hypothesis testing and uses several parallel statistical tests in which indeterminate decisions are allowed to insure the reliability and reasonableness of the final decision. To demonstrate the capability of the new algorithm three images processed with this algorithm and with the Sobel edge operator for comparison are shown. The new algorithm is shown to work well on noisy data and requires no preprocessing. The new algorithm detects connected edge segments instead of individual edge points, which results in cleaner edges.

Original languageEnglish (US)
Title of host publicationBiomedical Engineering Perspectives
Subtitle of host publicationHealth Care Technologies for the 1990's and Beyond
PublisherPubl by IEEE
Pages167-168
Number of pages2
Editionpt 1
ISBN (Print)0879425598
StatePublished - 1990
EventProceedings of the 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Philadelphia, PA, USA
Duration: Nov 1 1990Nov 4 1990

Publication series

NameProceedings of the Annual Conference on Engineering in Medicine and Biology
Numberpt 1
ISSN (Print)0589-1019

Other

OtherProceedings of the 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CityPhiladelphia, PA, USA
Period11/1/9011/4/90

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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