Variants of seeded region growing

Abstract

Seeded region growing (SRG) is a fast, effective and robust method for image segmentation. It begins with placing a set of seeds in the image to be segmented, where each seed could be a single pixel or a set of connected pixels. Then SRG grows these seeds into regions by successively adding neighbouring pixels to them. It finishes when all pixels in the image are assigned to one (and only one) region. The growing strategy of SRG implicitly assumes that pixels from the same region share the same greyvalue. As a first contribution, this study develops new modifications to SRG so that this constant greyvalue assumption is relaxed. Since the growing strategy of SRG does not impose any constraints or restrictions on the shapes of the growing regions, quite often SRG would produce very rough segmentation boundaries even the true boundaries were smooth. The second contribution of this study is the proposal of a stabilised SRG that encourages smoother boundaries and prevents the so-called leakage problem. All these new variants are conceptually simple and easy to implement. They are tested with simulated and real images, and are shown to have better performances than the original SRG.