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[[Region-growing]] methods rely mainly on the assumption that the neighboring pixels within one region have similar values. The common procedure is to compare one pixel with its neighbors. If a similarity criterion is satisfied, the pixel can be set to belong to the same cluster as one or more of its neighbors. The selection of the similarity criterion is significant and the results are influenced by noise in all instances.
The method of Statistical Region Merging<ref name="SRM">R. Nock and F. Nielsen, [http://www.academia.edu/download/3433683/Statistical_Region_Merging.pdf Statistical Region Merging]{{dead link|date=July 2022|bot=medic}}{{cbignore|bot=medic}}, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26, No 11, pp 1452–1458, 2004.</ref> (SRM) starts by building the graph of pixels using 4-connectedness with edges weighted by the absolute value of the intensity difference. Initially each pixel forms a single pixel region. SRM then sorts those edges in a priority queue and decides whether or not to merge the current regions belonging to the edge pixels using a statistical predicate.
One [[region-growing]] method is the seeded region growing method. This method takes a set of seeds as input along with the image. The seeds mark each of the objects to be segmented. The regions are iteratively grown by comparison of all unallocated neighboring pixels to the regions. The difference between a pixel's intensity value and the region's mean, <math>\delta</math>, is used as a [[Similarity measure|measure of similarity]]. The pixel with the smallest difference measured in this way is assigned to the respective region. This process continues until all pixels are assigned to a region. Because seeded region growing requires seeds as additional input, the segmentation results are dependent on the choice of seeds, and noise in the image can cause the seeds to be poorly placed.
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