Work

Curve matching on brain surfaces using frenet distances

Year: 1998

Type: article

Abstract: This paper describes methods for diffeomorphic matching of curves on brain surfaces. Distances between curves are defined by Frenet representation via speed, curvature, and torsion. The curvematching algorithm is based on bipartite graph matching, with weights defined by the Frenet distance over diffeomorphic maps of one curve onto the other (Sedgewick [1983]: Algorithms). We follow Khaneja ([1996]: Statistics and Geometry of Cortical Features) and define fundus curves on the brain surfaces as extremal curvature lines generated using dynamic programming. Examples are shown for fundus curve matchings on macaque brain surfaces.

Source: Human Brain Mapping

DOI: 10.1002/(sici)1097-0193(1998)6:5/6<329::aid-hbm1>3.0.co;2-x

Authors: M. Bakırcioğlu, U. Grenander, N. Khaneja and, M.I. Miller

Institutions: Johns Hopkins University, Brown University, Harvard University

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, National Institutes of Health, U.S. Public Health Service, National Institute of Neurological Disorders and Stroke

Awards: NORMAL MR NEUROMORPHOMETRY BY GLOBAL PATTERN MATCHING, G1980864619, G2329877137, Mathematical/Computational Tools for Mapping Brain Data Bases