Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking
This work addresses a specific challenge in medical image analysis and computer vision for tracking tubular structures, representing an incremental improvement over existing minimal paths-based approaches.
The paper tackled the problem of shortcuts and short branches in minimal paths-based tubular structure tracking by introducing a model that combines perceptual grouping with curvature regularization, achieving outperformance over state-of-the-art methods in experiments on synthetic and real images.
Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms.