DREAM3D User Manual
Identify Sample

Group (Subgroup)

Processing Filters (Cleanup)

Description

Often when performing a serial-sectioning experiment (especially in the FIB-SEM), the sample is "overscanned" resulting in a border of "bad" data around the sample. This Filter attempts to "identify" the sample within the overscanned volume. The filter makes the assumption that there is only one contiguous set of Cells that belong to the sample. The filter requires that the user has already thresheld the data to determine which Cells are "good" and which are "bad". The algorithm for the identification of the sample is then as follows:

  1. Search for the largest contiguous set of "good" Cells. (This is assumed to be the sample)
  2. Change all other "good" Cells to be "bad" Cells. (This removes the "speckling" of what was thresheld as "good" data in the outer border region).
  3. Search for the largest contiguous set of "bad" Cells. (This is assumed to be the outer border region)
  4. Change all other "bad" Cells to be "good" Cells. (This removes the "speckling" of what was thresheld as "bad" data inside of the sample).

Note: if there are in fact "holes" in the sample, then this filter will "close" them by calling all the Cells "inside" the sample "good". If the user wants to reidentify those holes, then use the MultiThreshold Cells Filter and use criteria of GoodVoxels = 1 and whatever other criteria would identify the "holes", as this will limit applying those criteria to within the sample and not the outer border region.

Parameters

None

Required DataContainers

Voxel

Required Arrays

Type Default Name Description Comment Filters Known to Create Data
Cell GoodVoxels Single Threshold (Cell Data) (Processing), Multi Threshold (Cell Data) (Processing)

Created Arrays

None

Authors

Copyright: 2012 Michael A. Groeber (AFRL),2012 Michael A. Jackson (BlueQuartz Software)

Contact Info: dream.nosp@m.3d@b.nosp@m.luequ.nosp@m.artz.nosp@m..net

Version: 1.0.0

License: See the License.txt file that came with DREAM3D.

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