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Proceedings Paper

Learning edge-defining thresholds for local binary segmentation
Author(s): Leiguang Gong; Casimir A. Kulikowski; Reuben S. Mezrich
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Paper Abstract

Selecting a globally effective threshold to define edges for local binary thresholding and segmentation of images presents major problems given the significant variability in intensity and edge statistics from image to image and study to study. Previously reported results of applying binary local thresholding have depended on the careful empirical choice of a threshold range adapted to a particular class of images. We have developed two new systematic methods that learn the edge-defining threshold from the gradient image generated by applying a gradient operator. The first method minimizes a criterion function, and the second takes advantage of local constancy properties of the intensity threshold as a function of a selected edge-defining threshold. An edge-defining threshold is then obtained for each sub- image, and a global threshold derived from them for the whole image. Experiments with MR images from phantoms and various human and animal studies have shown the effectiveness of this approach.

Paper Details

Date Published: 26 June 1992
PDF: 11 pages
Proc. SPIE 1660, Biomedical Image Processing and Three-Dimensional Microscopy, (26 June 1992); doi: 10.1117/12.59571
Show Author Affiliations
Leiguang Gong, Rutgers Univ. (United States)
Casimir A. Kulikowski, Rutgers Univ. (United States)
Reuben S. Mezrich, Laurie Imaging Ctr. and UMDNJ-RWJMS (United States)

Published in SPIE Proceedings Vol. 1660:
Biomedical Image Processing and Three-Dimensional Microscopy
Raj S. Acharya; Carol J. Cogswell; Dmitry B. Goldgof, Editor(s)

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