This article explains the basic principle of segmentation for people who are new to imaging
What is segmentation?
In a nutshell, segmentation is the process by which software converts the pixels in the image, which are just intensity measurements, into objects that we can use to characterize certain features of the objects. Those features can include counting how many objects there are, their morphological properties (volume, surface area, sphericity, etc), intensity characteristics to measure signal expression, or more complex inter-object relationships (number of children, distance to the nearest neighbor, etc).
Segmentation is generally a 2 step process where the pixels in the image are first classified as belonging to either the background or object class, and then the actual segments are created by establishing the relationship between the neighboring pixels. The simplest and most common kind of segmentation is based on an intensity threshold, where pixels are classified according to their value being above or below a given threshold and then contiguous groups of object class pixels are identified as objects.
To better understand this process consider this very simple image:
Humans have no issue intuitively recognizing 3 objects, but software doesn't work like that. What the software has is a matrix of intensities:
As a fun exercise, you can try to download this image as a CSV, import it into Excel, and use conditional formatting rules to color the pixels so that they are black if they have an intensity of 0, and white if they have an intensity of 255.
From this map of intensity values, we can set up a simple rule to the effect that all pixels with an intensity above 77 are part of the object class and then count all the contiguous groups of object class pixels as individual objects:
Most image analysis packages offer some form of threshold-based segmentation, including Vision4D, along with various other methods.
For more about the segmentation tools available in arivis, please check this article.