Thin Structure Segmentation and Visualization in Three-Dimensional Biomedical Images: A Shape-Based Approach

Adam Huang, Gregory M. Nielson, Anshuman Razdan, Gerald E. Farin, Debra Baluch, David Capco

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


This paper presents a shape-based approach in extracting thin structures, such as lines and sheets, from three-dimensional (3D) biomedical images. Of particular interest is the capability to recover cellular structures, such as microtubule spindle fibers and plasma membranes, from laser scanning confocal microscopic (LSCM) data. Hessian-based shape methods are reviewed. A synthesized linear structure is used to evaluate the sensitivity of the multiscale filtering approach in extracting closely positioned fibers. We find that the multiscale approach tends to fuse lines together, which makes it unsuitable for visualizing mouse egg spindle fibers. Single-scale Gaussian filters, balanced between sensitivity and noise resistance, are adopted instead. In addition, through an ellipsoidal Gaussian model, the eigenvalues of the Hessian matrix are quantitatively associated with the standard deviations of the Gaussian model. Existing shape filters are simplified and applied to LSCM data. A significant improvement in extracting closely positioned thin lines is demonstrated by the resultant images. Further, the direct association of shape models and eigenvalues makes the processed images more understandable qualitatively and quantitatively.

Original languageEnglish (US)
Pages (from-to)93-102
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
StatePublished - 2006


  • Angiography biomedical image processing
  • Hessian matrix
  • image enhancement
  • laser scanning confocal microscopy
  • multiscale filtering
  • segmentation
  • visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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