Here I describe how we segmentation digital image based on region. I think it help you.

1. Region-Based Image
Segmentation

2. Definition
Segmentation: It refers to the process of partitioning a image
into multiple regions.
Regions: A group of connected pixels with similar properties.
Regions are used to interpret images. A region may correspond to
a particular object, or different parts of an object.

3. Properties
oConnectivity and compactness
o Regularity of boundaries
o Homogeneity in terms of color or texture
o Differentiation from neighbor regions

4. Applications
oIn image compression
oObject recognition
oComputer graphics
oMedical Imaging
oMPEG-4 video object (VO) segmentation

5. Need of
segmentation
oThe goal of segmentation is to simplify the representation of an
image into something that is more meaningful and easier to
analyze.
oImage segmentation is typically used to locate objects and
boundaries in images.
oFor correct interpretation, image must be partitioned into regions
that correspond to objects or parts of an object.

6. Basic
Formulation
Let R represent the entire image region. We want to partition R
into n sub regions, R1,R2, . . ., Rn, such that:
o Summation of Ri =R
o Ri is a connected region for i=1, 2, . . , n
oRi intersection Rj =f for all i and j , I≠ j
oP(Ri) = TRUE for i=1, 2, . . . n
oP( Ri summation Rj)= False , i ≠ j

7. Segmentation
Effect
Region Segmented Image

8. Approaches to
Segmentation
Region based approaches
o Group together pixels with similar properties
o Combining proximity and similarity

9. Classification
Region based approaches are based on pixel properties
such as
o Homogeneity
o Spatial proximity
The most used methods are
o Thresholding
o Clustering
o Region growing
o Split and merge

10. Pixel
Aggregation
(Region
Growing)
The basic idea is to grow from a seed pixel
oAt a labeled pixel, check each of its neighbors
oIf its attributes are similar to those of the already labeled
opixel, label the neighbor accordingly
oRepeat until there is no more pixel that can be labeled
For example, let
oThe attribute of a pixel is its pixel value
oThe similarity is defined as the difference bet adjacent pixel values
oIf the difference is smaller than a threshold, assigned to the same
region, otherwise not

11. Region
Growing :
Algorithm
oChose or determined a group of seed pixel which can correctly
represent the required region
oFixed the formula which can contain the adjacent pixels in the
growth
oMade rules or conditions to stop the growth process

12. Region Split
and Merge
oSplit operation adds missing boundaries by splitting regions that
contain part of different objects.
oMerge operation eliminates false boundaries and spurious
regions by merging adjacent regions that to the same object.
Split-and-merge in a hierarchical data structure

13. Algorithm:
Region
Splitting
oCompute the variance in the gray values for the region
oIf the variance is above a threshold, split the region along the
appropriate boundary
oIf some property of a region is not constant
oDivide the region into a fixed number of equal-sized regions.

14. Algorithm:
Region
Merging
oForm initial regions in the image using thresholding ( or a similar
approach) followed by component labeling
oPrepare a region adjacency graph (RAG) for the image.
oFor each region in an image, perform the following steps:
(a) Consider its adjacent region and test to see if they are similar.
(b) For regions that are similar, merge them and modify the RAG.
oRepeat step 3 until no regions are merged.