Regularized Geometric Hulls for Bio-medical Image Segmentation

One of the most important and challenging tasks in bio-medical image analysis is the localization, identification, and discrimination of salient objects or structures. While to date human experts are performing these tasks manually at the expense of time and reliability, methods for automation of these processes are evidently called for. This paper outlines a novel technique for geometric clustering of related object evidence called regularized geometric hulls (RGH) and gives three exemplary real-world application scenarios. Several experiments performed on real-world data highlight a set of useful advantages, such as robustness, reliability, as well as efficient runtime behavior.