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5 Interesting Ways to Analyze 3D Images With Python
Explore image filtering, segmentation, object measurements, and visualization with a synthetic dataset
A three-dimensional image stores information across three spatial axes. We can think of it as a stack of two-dimensional images, where each element represents a small part of the volume. This element is called a voxel.
With Python, we can process the whole volume rather than analyze each slice separately. For example, we can reduce noise, separate objects from the background, and measure how much space each object occupies.
However, the measurement result depends on how we process the image. A small object could become smaller after filtering, or two nearby objects could become one connected region.
In this article, we will explore five techniques using the same synthetic volume. We would also compare the measurements with the objects we created, so we can see what changes when we adjust the parameters.
Let’s get into it.
Preparing the 3D Image
For this example, we would use NumPy, SciPy, scikit-image, and Matplotlib. First, install the packages in a virtual environment using the requirements file from the repository.
