Application of the Level-Set Model with Constraints in Image Segmentation

We propose and analyze a constrained level-set method for semi-automaticimage segmentation. Our level-set model with constraints on the level-set functionenables us to specify which parts of the image lie inside respectively outside thesegmented objects. Such a-priori information can be expressed in terms of upperand lower constraints prescribed for the level-set function. Constraints have thesame conceptual meaning as initial seeds of the popular graph-cuts based methodsfor image segmentation. A numerical approximation scheme is based on thecomplementary-finite volumes method combined with the Projected successive overrelaxationmethod adopted for solving constrained linear complementarity problems.The advantage of the constrained level-set method is demonstrated on severalartificial images as well as on cardiac MRI data.