A Partition Constrained Minimization Scheme for Efficient Multiphase Level Set Image Segmentation

This study investigates a new multiphase minimization scheme which embeds a simple, efficient partition constraint directly in multiple level set evolution. Starting from an arbitrary initial partition, the minimization of the N-region segmentation functional is carried out following a first order expansion of the data term with an embedded partition constraint: if a point leaves a region, it goes to single other region. The method has a computational advantage over previous multiphase schemes, is stepwise optimal, i.e., permit to effect the maximum decrease of the functional at each evolution step, and is robust to initialization. The method is discussed by comparison with previous methods and experimental results are included to this effect.