Revision as of 04:25, 29 June 2009

Feature computation for Trace Editor

Feature List

Algorithms

The algorithms suggested are used to control the editing process allowing for rule based cluster editing. The Goal is to complete group editing in five steps.

Proposed steps of editing

1: Merge Small Gaps
Goal: Create longest continuous trace segments by merging close endpoints
Methods: Nearest neighbors  (Closest end points), 
Rejection based on conflicts and thresholds 

• Minimal distance between end points
  • Angle between traces- Consider the direction vector for two endpoint e₁, e₂ in proximity( this is given by a constant); the direction vectors are d₁ and d₂ respectfully. Using this information we can find the angular change by projecting d₁ onto d₂, the angular change can be found using the error vector and the direction vectors. We can also draw a triangle using the line connecting e₁ and e₂, the lines through d₁ and d₂, and the midpoint line. The second method as of now requires the picture to be projected to 2 dimensions, but this should be easy to extend.
  • Path length
  • Gap distance is too large
• Another trace is a better fit (Cost Function)
  • Smallest gap
  • Better Angular alignment
• "Bad merge"
  • The merge causes corners
  • Needs to be smoothed

2: Interpolate Large Gaps
Goal: Connect Large gaps that step 1 cannot  simply connect by addition of a  single cell
Method: Larger gaps need new segments created, 
new Trace Bits must be added, 
smoothing operator.

Example showing how Merging and branching are interconnected. The Merging that creates sharp turns needs to be smoothed. The Traces need to create a new branch point and trace bits to interpolate the original *Curve fitting to find trend of: **Direction **Curvature *Interpolation **Extend the line **Most probable vector **Avoid creating edges path of the branch.

3: Branch Points
Goal: Detect and control Branching
Method: Detect most probable intercepts, 
Determination of main branch and child, 
Type of branch point

• Distance maps
  • Nearest neighbors (traces)
  • Closest trace bits
• Angle of intercept
• Interpolation

4: Soma Detection
Goal: Correspond processes with a soma
Method: Segmentation of original data, 
Localize the area to attach processes to soma, 
Correct direction of traces

• Image intensity
• Blob segmentation
• Centroid
• Distance map
• Connectivity
  • Connected components
  • Localization of processes

Illustration of how the processes should be connected to a soma(shown in red).
The Dendrites are shown in blue, where the axon is shown in yellow.

5: Fragments
Goal: Removal of small traces that do not correspond to a process
Method: Small traces removed, 
Leftovers from splitting operators, 
Line fragments that cannot be merged

• Lowest percentile of length
  • Traces with no parents or children
  • Type dependent