Segmentation in ImageJ/Fiji
The Basics of Segmentation Methods using ImageJ-based Tools
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?
Segmentation using Machine Learning...
Segmentation using Machine Learning...
Segmentation using Machine Learning...
Segmentation using Machine Learning...
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
Getting Started with Segmentation
Getting Started with Segmentation
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
Getting Started with Segmentation
How to Segment?
Real World Examples
Real World Examples
Real World Examples
Real World Examples
Real World Examples
Helpful Resources: