Line and net pattern segmentation using shape modeling

Adam Huang, Gregory Nielson, Anshuman Razdan, Gerald Farin, David Capco, Page Baluch

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


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 languageEnglish
Pages (from-to)171-180
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2003
EventVisualization and Data Analysis 2003 - Santa Clara, CA, United States
Duration: 21 Jan 200322 Jan 2003


  • Crest line
  • Curvature
  • Derivative
  • Feature extraction
  • Image enhancement
  • Image processing
  • Image segmentation
  • Matched filter


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