Self-Repelling Snakes for Topology-Preserving Segmentation Models

• Published in: IEEE transactions on image processing Vol. 17; no. 5; pp. 767 - 779
• Main Authors: Le Guyader, C., Vese, L.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive operator splitting (AOS) scheme; Algorithms; Application software; Applied sciences; Artificial Intelligence; Biological and medical sciences; Biomedical imaging; Computer science; control theory; systems; Computer Simulation; Computer vision; Computerized, statistical medical data processing and models in biomedicine; Cortexes; Exact sciences and technology; geodesic active contours; Human; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Image reconstruction; Image segmentation; Information, signal and communications theory; level-set method; Mathematical models; Mathematics; Medical management aid. Diagnosis aid; Medical sciences; Merging; Models, Statistical; Numerical Analysis; Numerical Analysis, Computer-Assisted; Pattern recognition; Pattern Recognition, Automated - methods; Pattern recognition. Digital image processing. Computational geometry; Process design; Propagation; Reconstruction; Reproducibility of Results; Segmentation; Sensitivity and Specificity; Shape; Signal processing; Signal Processing, Computer-Assisted; Studies; Telecommunications and information theory; Topology; topology preservation; Computer vision; Target tracking; Image processing; Additive operator splitting (AOS) scheme; Shape detection; Flexibility; Image segmentation; geodesic active contours; level-set method; topology preservation; Geodesic; Medical imagery; segmentation; Edge detection; Active contour; additive operator splitting (AOS) scheme
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2008.919951

The implicit framework of the level-set method has several advantages when tracking propagating fronts. Indeed, the evolving contour is embedded in a higher dimensional level-set function and its evolution can be phrased in terms of a Eulerian formulation. The ability of this intrinsic method to handle topological changes (merging and breaking) makes it useful in a wide range of applications (fluid mechanics, computer vision) and particularly in image segmentation, the main subject of this paper. Nevertheless, in some applications, this topological flexibility turns out to be undesirable: for instance, when the shape to be detected has a known topology, or when the resulting shape must be homeomorphic to the initial one. The necessity of designing topology-preserving processes arises in medical imaging, for example, in the human cortex reconstruction. It is known that the human cortex has a spherical topology so throughout the reconstruction process this topological feature must be preserved. Therefore, we propose in this paper a segmentation model based on an implicit level-set formulation and on the geodesic active contours, in which a topological constraint is enforced.