Segmentation in ImageJ
The Basics of Segmentation Methods/Tools using ImageJ
What is Segmentation?
What is Segmentation?
The partitioning of a digital image into multiple segments.
Common assumptions/requirements:
Common assumptions/requirements:
2. Do you want to count objects? Or do you want to segment regions?
Common assumptions/requirements:
3. What are the shapes of your objects? Tubes? Blobs? Clouds?
Common assumptions/requirements:
4. What modality was used? And how does the signal look?
Machine Learning in Segmentation...
Machine Learning in Segmentation...
Machine Learning in Segmentation...
Machine Learning in Segmentation...
How to Segment?
How to Segment?
A Typical Pipeline
1. Preprocess
2. Threshold
3. Cleanup
4. Identify
5. Measure
How to Segment?
1. Preprocess (Link to sample image)
Improve signal-to-noise ratio
Find Edges | Kuwahara | Gaussian Blur | Median | Background Correction | Deconvolution
How to Segment?
How to Segment?
3. Cleanup
Improve mask with morphological operations
Erode | Dilate | Open | Close | Skeletonize | Outline | Fill Holes | Watershed | MorphoLibJ | Morphological Segmentation | Distance Transform Watershed
How to Segment?
4. Identify
Split into individual objects
Analyze Particles | Extended Particle Analyzer
How to Segment?
How to Segment?
Real World Examples
Real World Examples
Real World Examples
Real World Examples
Real World Examples
Helpful Resources: