Region based segmentation divides an image into parts based on different rules. There are four types of region based segmentation: region growing, region splitting, region merging, and region splitting and merging. Global thresholding segments an image into foreground and background pixels using a single threshold value applied to the entire image histogram. The threshold value can be selected manually or automatically using an algorithm that calculates the average of the foreground and background pixel values. Adaptive thresholding calculates multiple threshold values for different regions of an image, rather than applying a single threshold to the whole image as in global thresholding. This allows thresholding to adapt to different lighting conditions within an image.

2. Region based segmentation
 Segmentation is process in which we use to divide the image in different parts and then we apply
different rules on it.
 Separating region’s and performing operation on it with the help of given rules in the operation
 So to make a suitable image segmentation
 There are three types of region based segmentation
 1. Region growing segmentation
 2. Region splitting segmentation
 3.Region merging segmentation
 4. Region splitting and merging segmentation

4. Global Thresholding
 This is the simplest of all thresholding.
 Here the histogram of the complete image is partitioned by a single threshold T.
 Segmentation is then achieved by scanning each pixel and labelling it as background or
foreground depending on the Gray level of that pixel.
 The success of this method depends on the entirely on how well the histogram can be
portioned.
 If Gray level less than T it is labelled as black(0) else it is labelled as white (255).
 Example = camscanner

5. Global Thresholding
 We have to define the value of T based on visual inspection of histogram,
using the following algorithm we can find the value of T automatically.
 1. Select an initial estimate of T.
 2. segment the image using T. this will produce 2 group of pixels
G1 <= T & G2 >= T
 3.Compute the average Gray level values µ1 and µ2 for pixels in regin G1
and G2
 4.Compute new threshold T = ½(µ1 + µ2)

6.  5. Repeat steps 2 & 4 until the difference in T is successive iteration is smaller then a
predefined parameter to.

8. Adaptive Thresholding
 Adaptive thresholding is the method where the threshold value is calculated for
smaller regions and therefore, there will be different threshold values for different
regions.