Image segmentation

image segmentation :
The image segmentation approaches can be categorized into two types based on properties of image.
A. Discontinuity detection based approach:
B.Similarity detection based approach:

Similarity detection is an approach which is based on finding the similar region of an image. The following are the techniques that come under this are: region growing, thresholding techniques and region merging and splitting.

All these techniques divide an image with the help of similar pixels. This approach is also used for clustering the data. In this approach, clusters of pixels are formed that have similar features.
So image segmentation has mainly three perspective approaches.
These are:
1. Region Approach: This falls under similarity
detection approach.
2. Edge detection and Data Clustering Approach:
Edge detection approach comes under discontinuity detection approach but data clustering comes similarity detection based approach.

A. Thresholding Method

B. Edge Based Segmentation method

C. Region Based Segmentation Method:

1)Region growing methods:
2)Split and Merge:
D. Clustering Based Segmentation Method

1) Hard Clustering:
2) Soft Clustering:

E.Watershed Based Method
The watershed based method uses the concept of topological interpretation. In this the intensity represents the basins having hole in its minima from where the water spills. When water reaches the border of basin the adjacent basins are merged together. To maintain separation between basins dams are required and are the borders of region of segmentation. These dams are constructed using dilation.  The watershed methods consider the gradient of image as topographic surface. The pixels having more gradient are represented as boundaries which are continuous.  F. Partial Differential Equation Based Segmentation Method. 
G.Artificial Neural Network