1Local Optimization based Segmentation of Spatially-Recurring, Multi-Region Objects with Part Configuration Constraints

Abstract—The level set framework has been a popular medical image segmentation technique for many years due to its several advantages, such as parametrization independence, ease of im-plementation, extendibility from a curve in 2D to higher dimen-sions, and automatic handling of topological changes. However, existence of noise, low contrast and objects complexity in medical images cause many segmentation algorithms (including level set-based methods) to fail. Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results. Two important constraints, containment and exclusion of regions, have gained attention in recent years mainly due to their descriptive power and intuitive definitions. In this paper, we augment the level set framework with the ability to handle these two intuitive geometric relationships, containment and exclusion, along with a distanc