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Proceedings Paper

Line and net pattern segmentation using shape modeling
Author(s): Adam Huang; Gregory M. Nielson; Anshuman Razdan; Gerald Farin; David Capco; Page Baluch
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Paper Abstract

Line and net patterns in a noisy environment exist in many biomedical images. Examples include: Blood vessels in angiography, white matter in brain MRI scans, and cell spindle fibers in confocal microscopic data. These piecewise linear patterns with a Gaussian-like profile can be differentiated from others by their distinctive shape characteristics. A shape-based modeling method is developed to enhance and segment line and net patterns. The algorithm is implemented in an enhancement/thresholding type of edge operators. Line and net features are enhanced by second partial derivatives and segmented by thresholding. The method is tested on synthetic, angiography, MRI, and confocal microscopic data. The results are compared to the implementation of matched filters and crest lines. It shows that our new method is robust and suitable for different types of data in a broad range of noise levels.

Paper Details

Date Published: 9 June 2003
PDF: 10 pages
Proc. SPIE 5009, Visualization and Data Analysis 2003, (9 June 2003); doi: 10.1117/12.477535
Show Author Affiliations
Adam Huang, Arizona State Univ. (United States)
Gregory M. Nielson, Arizona State Univ. (United States)
Anshuman Razdan, Arizona State Univ. (United States)
Gerald Farin, Arizona State Univ. (United States)
David Capco, Arizona State Univ. (United States)
Page Baluch, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 5009:
Visualization and Data Analysis 2003
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Matti T. Groehn; Katy Boerner, Editor(s)

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