Multi-Dimensional Image Segmentation Using Seed-Invariant Region Growing

Abstract

The goal of image segmentation is to partition a digital image into disjoint regions of interest. Of the many proposed image-segmentation methods, region growing has been one of the most popular. Research on region growing, however, has focused primarily on the design of feature measures and on growing and merging criteria. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points. We define a set of theoretical criteria for a subclass of region-growing algorithms that are insensitive to the selection of the initial growing points. This class of algorithms, referred to as Symmetric Region Growing, leads to a singlepass region-growing approach applicable to any dimensionality of images. Furthermore, they lead to region-growing algorithms that are both memory- and computation-efficient. Finally, by-products of this general paradigm are algorithms for fast connected-component labeling and cavity deletion. The paper gives complete theoretical results and 3-D image examples.