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Proceedings Paper

Segmentation algorithm for objects with very low edge contrast
Author(s): Stephen McCarthy; Timothy C. Miller; Isaac N. Bankman
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Paper Abstract

We present an algorithm for segmentation of objects with very low edge contrast, such as microcalcifications in mammogram images. Most methods used to segment microcalcifications have algorithmic aspects that could raise operational difficulties, such as thresholds or windows that must be selected manually, or parametric models of the data. The presented algorithm does not use any of these techniques and does not require that any parameters be set by a user. It builds upon an earlier algorithm presented, but is much faster and also applicable to a wider range of objects to be segmented. The algorithm’s approach is based on the extension of radial intensity profiles from a given seed point to the edge of the image. A first derivative analysis is used to find an edge point pixel along each directional intensity profile. These points are connected and the resulting object border is filled using a constrained dilatation operation to form a complete region. Results from the tested mammography images indicate that the segmented regions compare closely to those expected from visual inspection.

Paper Details

Date Published: 25 October 2004
PDF: 7 pages
Proc. SPIE 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, (25 October 2004); doi: 10.1117/12.580136
Show Author Affiliations
Stephen McCarthy, Johns Hopkins Univ. (United States)
Timothy C. Miller, Johns Hopkins Univ. (United States)
Isaac N. Bankman, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 5608:
Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Roning, Editor(s)

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