MINI TUTORIALS
X-ray CT Explained with ImageJ
This Mini Tutorial series explains basic concepts of X-ray CT such as reconstruction, denoising, segmentation, and quantitative analysis using the open-source "ImageJ." You can download ImageJ and sample images for each episode in the resource link section below the video. Email us at imaging@rigaku.com if you have any questions or requests for future Mini Tutorial topics.
Resource links
1. ImageJ - Getting started guide
- Download Fiji distribution of ImageJ: https://imagej.net/software/fiji/downloads
- Download sample images: X-ray CT explained with ImageJ sample data 1
2. Basic image processing - Denoising
- Download sample images: X-ray CT explained with ImageJ sample data 2
3. Basic quantitative analysis
- Download sample images: X-ray CT explained with ImageJ sample data 3
4. Changing file sizes
- Download sample images: X-ray CT explained with ImageJ sample data 4
5. Machine learning segmentation by Weka
- Learn more about Weka: https://www.cs.waikato.ac.nz/ml/weka/
- Download sample images: X-ray CT explained with ImageJ sample data 5
6. Understanding "resolution"
- Learn more about Shannon Nyquist Sampling Theorem: https://www.youtube.com/watch?v=FcXZ28BX-xE&t=38s
- Download sample images: X-ray CT explained with ImageJ sample data 6
7. How reconstruction works
- Download Radon transform plugin: https://imagej.nih.gov/ij/plugins/radon-transform.html
- Download sample images: X-ray CT explained with ImageJ sample data 7
You might also like:
- X-ray Computed Tomography Virtual Workshop - CT Data Analysis Using ImageJ
- X-ray Computed Tomography Virtual Workshop - Viewers Choice - CT Data Analysis Using ImageJ
- "Image Processing with ImageJ" by José María Mateos Perez and Javier Pascau. This is a good overview of ImageJ functions. You can read it from the beginning or use it as a handbook.
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