Region Growing

Region growing approach is the opposite of the split and merge approach:

• An initial set of small areas are iteratively merged according to similarity constraints.
  
• Start by choosing an arbitrary seed pixel and compare it with neighbouring pixels (see Fig 37).
  
• Region is grown from the seed pixel by adding in neighbouring pixels that are similar, increasing the size of the region.
  
• When the growth of one region stops we simply choose another seed pixel which does not yet belong to any region and start again.

• This whole process is continued until all pixels belong to some region.
  
• A bottom up method.

Region growing methods often give very good segmentations that correspond well to the observed edges.

 

Fig. 37 Example of region growing

However starting with a particular seed pixel and letting this region grow completely before trying other seeds biases the segmentation in favour of the regions which are segmented first.

This can have severalundesirable effects:

• Current region dominates the growth process -- ambiguities around edges of adjacent regions may not be resolved correctly.
  
• Different choices of seeds may give different segmentation results.
  
• Problems can occur if the (arbitrarily chosen) seed point lies on an edge.

To counter the above problems, simultaneous region growing techniques have been developed.

• Similarities of neighbouring regions are taken into account in the growing process.
  
• No single region is allowed to completely dominate the proceedings.
  
• A number of regions are allowed to grow at the same time.
  
  
  • similar regions will gradually coalesce into expanding regions.
  
  
• Control of these methods may be quite complicated but efficient methods have been developed.
  
• Easy and efficient to implement on parallel computers.