Region Based Segmentation

In image processing, a technique known as "region-based segmentation" divides pixels into meaningful sections according to shared characteristics such as color, texture, intensity, or other attributes. Whereas edge-based segmentation concentrates on discontinuities, region-based techniques look for related components that share comparable traits.

Key Approaches

There are primarily two main approaches to region-based segmentation

Region Growing

Idea: Beginning with a seed pixel, the zone is iteratively expanded to include nearby pixels if they meet a predetermined similarity requirement.
Procedure: Pick a starting pixel.
Compare the attributes of the region with the nearby pixels.
If the pixel is similar, add it to the area; if not, remove it.
Continue until there are no more pixels to add.
Difficulties: Highly susceptible to noise, selection of seed pixels, and uniformity requirements.

Region Splitting and Merging

Idea: Segments an image into smaller parts, which are subsequently combined or separated according to homogeneity standards.
Procedure: At first, the picture is seen as a single region in its whole.
If a region is heterogeneous, divide it into smaller regions.
If neighboring regions are homogeneous, combine them.
Continue iterating until the desired division is attained.
Choosing suitable criteria for splitting and merging, as well as computing complexity, provide challenges.
 

Other Techniques

Watershed Algorithm: The image is treated as a topographic map via the Watershed Algorithm, where pixels stand in for elevation. Watersheds shape regions and water is poured at local minima.
Mean Shift: An approach for non-parametric clustering that locates dense areas inside the feature space.

Applications

There are several uses for region-based segmentation, such as:

• Analysis of medical images (segmenting tumors and organs)
• Identifying and detecting objects
• compression of images
• Using remote sensing
• examination of videos