Application of the level-set model with constraints in image segmentation

TL;DR

This paper introduces a constrained level-set method for semi-automatic image segmentation that incorporates prior information through constraints, improving segmentation accuracy on artificial and MRI images.

Contribution

It presents a novel constrained level-set model with a numerical scheme for image segmentation, integrating a-priori constraints similar to seed-based methods.

Findings

Effective on artificial images

Demonstrated on cardiac MRI data

Improves segmentation accuracy

Abstract

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.