Split-and-Merge Segmentation of Biomedical Images Using Graph Wedgelet Decompositions

Graph wedgelets are a novel tool for the fast decomposition of images in geometrically meaningful, wedge-shaped, subregions. In this work, we study the usage of graph wedgelets as a promising splitting method in a split-and-merge segmentation scheme for images. We combine adaptive wedgelet splits of images with a simple and classical merging strategy for subregions, and obtain in this way an efficient and robust segmentation of relevant subdomains, that can be used in the segmentation of biomedical images obtained by modalities as, for instance, Magnetic Resonance Imaging.