Abstract
The major drawback of watershed transformation is over-segmentation. It also has a significant advantage: very good edge extraction. Thresholding methods usually utilize only global information such as an image histogram; however, they have the ability to group pixels into clusters by their value. The method presented in this paper combines the advantages of watershed segmentation and multilevel thresholding. This was achieved by modifying selected optimal thresholding methods so that they treat watersheds as a whole and using those methods in a multilevel thresholding algorithm for grouping watersheds. Otsu's, Kapur's, maximum entropy and step function approximation thresholding methods have been tested. The obtained results are presented and discussed.
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