Application of the level-set model with constraints in image segmentation
This work addresses the need for interactive image segmentation with user-defined constraints, offering an alternative to graph-cuts methods for medical imaging applications.
The paper proposes a constrained level-set method for semi-automatic image segmentation that allows users to specify constraints on which parts of the image lie inside or outside objects. The method is demonstrated on artificial images and cardiac MRI data, showing improved segmentation accuracy over unconstrained level-set methods.
We propose and analyze a constrained level-set method for semi-automatic image segmentation. Our level-set model with constraints on the level-set function enables us to specify which parts of the image lie inside respectively outside the segmented objects. Such a-priori information can be expressed in terms of upper and lower constraints prescribed for the level-set function. Constraints have the same conceptual meaning as initial seeds of the popular graph-cuts based methods for image segmentation. A numerical approximation scheme is based on the complementary-finite volumes method combined with the Projected successive over-relaxation method adopted for solving constrained linear complementarity problems. The advantage of the constrained level-set method is demonstrated on several artificial images as well as on cardiac MRI data.