Watershed—based Image Segmentation with Region Merging and Edge Detection

The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obhtain primary classified image into different intensity regions.A watershed transformation technique is then employes.This includes:gradient of the classified image,dividing the image into markers,checking the Marker Image to see if it has zero points(watershed lines).The watershed lines are then deleted in the Marker Image created by watershed algorithm.A Region Adjacency Graph (RAG)and Region Adjacency Boundary(RAB)are created between two regions from Marker Image.Finally region merging is done according to region average intensity and two edge strengths (T1,T2).The approach of the authors is tested on remote sensing and brain MR medical images.The final segmentation result is one closed boundary per actual region in the image.

Watershed-based Image Segmentation with Region Merging and Edge Detection

The clustering technique is used to examine each pixel in the image which assigned to one of the clusters depending on the minimum distance to obtain primary classified image into different intensity regions. A watershed transformation technique is then employes. This includes: gradi-ent of the classified image, dividing the image into markers, checking the Marker Image to see if it has zero points (watershed lines). The watershed lines are then deleted in the Marker Image created by watershed algorithm. A Region Adjacency Graph (RAG) and Region Adjacency Boundary (RAB) are created between two regions from Marker Image. Finally region merging is done according to region average intensity and two edge strengths ( T1, T2). The approach of the authors is tested on remote sensing and brain MR medical images. The final segmentation re-sult is one closed boundary per actual region in the image.