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.
Original language | English (US) |
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Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
Editors | R.F. Erbacher, P.C. Chen, J.C. Roberts, M.T. Grohn, K Borner |
Pages | 171-180 |
Number of pages | 10 |
Volume | 5009 |
DOIs | |
State | Published - 2003 |
Event | Visualization and Data Analysis 2003 - Santa Clara, CA, United States Duration: Jan 21 2003 → Jan 22 2003 |
Other
Other | Visualization and Data Analysis 2003 |
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Country/Territory | United States |
City | Santa Clara, CA |
Period | 1/21/03 → 1/22/03 |
Keywords
- Crest line
- Curvature
- Derivative
- Feature extraction
- Image enhancement
- Image processing
- Image segmentation
- Matched filter
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics