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Proceedings Paper

Edge Detection In Cytology Using Local Statistical Properties.
Author(s): Joseph Barba; Paul Fenster; Henrick Jeanty; Joan Gil
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Paper Abstract

Accurate edge detection is a fundamental problem in the areas of image processing and pattern recognition/classification. The lack of effective edge detection methods has slowed the application of image processing to many areas, in particular diagnostic cytology, and is a major factor in lack of acceptance of image processing in service oriented pathology. In this paper, we present a two step procedure which detects edges. Since most images are corrupted by noise and often contain artifacts, the first step is to cleanup the image. Our approach is to use a median filter to reduce noise and background artifacts. The second operation is to locate image pixels which are "information rich" by using local statistics. This, step locates the regions of the image most likely to contain edges. The application of a threshold can then pinpoint those pixels forming the edge of structures of interest. The procedure has been tested on routine cytologic specimens.

Paper Details

Date Published: 16 December 1988
PDF: 10 pages
Proc. SPIE 0974, Applications of Digital Image Processing XI, (16 December 1988); doi: 10.1117/12.948480
Show Author Affiliations
Joseph Barba, City College of New York (United States)
Paul Fenster, City College of New York (United States)
Henrick Jeanty, City College of New York (United States)
Joan Gil, Mount Sinai Medical Center (United States)

Published in SPIE Proceedings Vol. 0974:
Applications of Digital Image Processing XI
Andrew G. Tescher, Editor(s)

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