Rapid pedobarographic image registration based on contour curvature and optimization

• Type: Article in International Scientific Journal
• Date: 2009

Authors

Francisco P. M. Oliveira (Author) Other; João Manuel R. S. Tavares (Author) FEUP; Todd C. Pataky (Author) Other

Journal

Title: Journal of Biomechanics

Vol. 42 No. 15

Pages: 2620-2623

ISSN: 0021-9290

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 16 Citations

Scopus - 19 Citations

Scientific classification

CORDIS: Technological sciences > Engineering > Mechanical engineering

FOS: Engineering and technology > Mechanical engineering

Other information

Authenticus ID: P-003-EC7

DOI: 10.1016/j.jbiomech.2009.07.005

Abstract (EN): Image registration, the process of optimally aligning homologous structures in multiple images, has recently been demonstrated to support automated pixel-level analysis of pedobarographic images and, subsequently, to extract unique and biomechanically relevant information from plantar pressure data. Recent registration methods have focused on robustness, with slow but globally powerful algorithms. In this paper, we present an alternative registration approach that affords both speed and accuracy, with the goal of making pedobarographic image registration more practical for near-real-time laboratory and clinical applications. The current algorithm first extracts centroid-based curvature trajectories from pressure image contours, and then optimally matches these curvature profiles using optimization based on dynamic programming. Special cases of disconnected images (that occur in high-arched subjects, for example) are dealt with by introducing an artificial spatially linear bridge between adjacent image clusters. Two registration algorithms were developed: a 'geometric' algorithm, which exclusively matched geometry, and a 'hybrid' algorithm, which performed subsequent pseudo-optimization. After testing the two algorithms on 30 control image pairs considered in a previous study, we found that, when compared with previously published results, the hybrid algorithm improved overlap ratio (p = 0.010), but both current algorithms had slightly higher mean-squared error, assumedly because they did not consider pixel intensity. Nonetheless, both algorithms greatly improved the computational efficiency (25 +/- 8 and 53 +/- 9ms per image pair for geometric and hybrid registrations, respectively). These results imply that registration-based pixel-level pressure image analyses can, eventually, be implemented for practical clinical purposes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4