Image Segmentation is the process of subdividing an image into its constituent regions or objects. In many computer vision applications, image segmentation is very useful to detect the region of interest. For instance, in medical imaging where we have to locate tumors, or in object detection like self-driving cars have to detect pedestrians, traffic signals, etc or for video surveillance, etc. There are a number of methods available to perform image segmentation. For instance, thresholding, clustering methods, graph partitioning methods, and convolutional methods to mention a few.

In this blog, we will discuss Image Thresholding which is one of the simplest methods for image segmentation. In this, we partition the images directly into regions based on the intensity values. So, let’s discuss image thresholding in greater detail.

Concept

If the pixel value is greater than a threshold value, it is assigned one value (maybe white), else it is assigned another value (maybe black).

In other words, if f(x,y) is the input image then the segmented image g(x,y) is given by

If the threshold value T remains constant over the entire image, then this is known as global thresholding. When the value of T changes over the entire image or depends upon the pixel neighborhood, then this is known as adaptive thresholding. We will cover both these types in greater detail in the following blogs.

Applicability Condition

Thresholding is only guaranteed to work when a good contrast ratio between the region of interest and the background exists. Otherwise, the thresholding will not be able to fully detect the region of interest. Let’s understand this by an example.

Suppose we have two images from which we want to segment the square region (our region of interest) from the background.

Let’s plot the histogram of these two images.

Clearly as expected for “A“, the histogram is showing two peaks corresponding to the square and the background. The separation between the peaks shows that the background and ROI have a good contrast ratio. By choosing a threshold value between the peaks, we will be able to segment out the ROI. While for “B”, the intensity distribution of the ROI and the background is not that distinct. Thus we may not be able to fully segment the ROI.

Thresholded images are shown below (How to choose a threshold value will be discussed in the next blog).

So, always plot the image histogram to check the contrast ratio between the background and the ROI. Only if the contrast ratio is good, choose the thresholding method for image segmentation. Otherwise, look for other methods.

In the next blog, we will discuss global thresholding and how to choose the threshold value using the iterative method. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.