Region Growing: When Simplicity Meets Theory - Region Growing Revisited in Feature Space and Variational Framework

Region growing is one of the most intuitive techniques for image segmentation. Starting from one or more seeds, it seeks to extract meaningful objects by iteratively aggregating surrounding pixels. Starting from this simple description, we propose to show how region growing technique can be elevated to the same rank as more recent and sophisticated methods. Two formalisms are presented to describe the process. The first one derived from non-parametric estimation relies upon feature space and kernel functions. The second one is issued from a variational framework, describing the region evolution as a process which minimizes an energy functional. It thus proves the convergence of the process and takes advantage of the huge amount of work already done on energy functionals. In the last part, we illustrate the interest of both formalisms in the context of life imaging. Three segmentation applications are considered using various modalities such as whole body PET imaging, small animal μCT imaging and experimental Synchrotron Radiation μCT imaging. We will thus demonstrate that region growing has reached this last decade a maturation that offers many perspectives of applications to the method.