Mini Tutorials - X-ray CT Explained with ImageJ

In this episode, you will learn where to go to download ImageJ and how to execute basic operations.

If you are new to CT image processing or ImageJ, this is a good place to start. You'll be able to follow the tutorial to set up ImageJ on your computer and practice data operations using the sample files.

In this episode, you will learn about various denoising filters and how to use them when segmenting images.

You can download all the tools and data used in the tutorial. Changing denoising parameters and seeing what happens to the images is one of the best ways to understand how this type of image processing works.

The next step that comes after segmentation is quantification. In this episode, you will learn how to quantify the image analysis results and extract useful parameters.

You can also explore the resources to learn how to execute more complex analyses, such as pore size distribution and fiber orientation.

CT data tend to be large, and you might need to reduce the file size for archiving or faster analysis. In this episode, you will learn how to change file sizes.

You will also see tips on managing CT data storage and selecting the right computer for CT data analysis in the resource section.

You can use machine learning to segment images with a high level of noise or uneven gray level that makes threshold-based segmentation challenging. In this episode, you will learn how to use the machine learning ImageJ plugin Weka.

In the resource section, you will also find webinars and workshops on deep learning image segmentation.