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Spot Detector 3D

Classical Watershed 3D Algorithm

Pre-filters to apply before this algorithm

  • Noise reduction filters (like median)
  • Laplacian of Gaussian that can be found in Misc Filters 3D gives very good results

The Algorithm

Description:

  • The watershed map is the maximum eigen value of the hessian transform (for details see featureJ page)
  • The watershed is seeded with Regional Minima of the watershed map, in order to limit over-segmentation
  • Propagation : propagation is done in the ascending order of the watershed map, starting from the seeds. It is a 3D 6-connectivity propagation.
  • Propagation is done until a user defined global threshold is reached.

Over-segmentation

This algorithm can produce over-segmentation. This can be corrected with (at least) 2 operations:
  • Reducing the noise during before computing the watershed map
  • Using the post-filter Merge Regions

Post-filters

  • As the propagation stops at a global threshold, a local threshold could be adjusted for each object. the post-filter Region Adjustment could be used.
  • Classical morphological operations

Usage:

  • Spots-like objects of small size like centromeres or genes