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.
1. ImageJ - Getting started guide
Mini Tutorial series Episode 1 starts with where to go to download open source FIJI/ImageJ and reviews how to open files, adjusting contrast, adjusting scale, taking measurements, and displaying a 3D rendering of a CT scan. You can download the sample images to follow the tutorial.- 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
Mini Tutorial series Episode 2 reviews different types of denoising filters, their side-effects on the sharpness and resolution of the image, and how these filters help improve the segmentation process. The tutorial uses open source FIJI/ImageJ and the images used in the tutorial are available for download.- Download sample images: X-ray CT explained with ImageJ sample data 2
3. Basic quantitative analysis
Mini Tutorial series Episode 3 reviews how we can analyze CT scans quantitatively using the image segmentation results and how we need to treat 2D and 3D images differently. The tutorial uses open source FIJI/ImageJ and the images used in the tutorial are available for download.- Download sample images: X-ray CT explained with ImageJ sample data 3
4. Changing file sizes
Mini Tutorial series Episode 4 reviews how to calculate a file sizes of CT images and how to reduce them when necessary to speed up the image analysis process. The tutorial uses open source FIJI/ImageJ and the images used in the tutorial are available for download.- Download sample images: X-ray CT explained with ImageJ sample data 4
5. Machine learning segmentation by Weka
Mini Tutorial series Episode 5 shows how you can use machine learning (Weka) to segment challenging images with noise and uneven gray levels. The tutorial uses open source FIJI/ImageJ and the images used in the tutorial are available for download.- 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
Mini Tutorial series Episode 6 explores the concept of resolution and discusses the optimal sampling size, effect of contrast on resolution, and the consequences of insufficient resolution. The tutorial uses open source FIJI/ImageJ and the images used in the tutorial are available for download.- 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
Mini Tutorial series Episode 7 explains how the filtered back projection reconstruction works by generating Radon transform of an image and reconstructing the original image. The tutorial uses open source FIJI/ImageJ and the images used in the tutorial are available for download.- 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
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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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