A Generalized Asymmetric Dual-front Model for Active Contours and Image Segmentation

TL;DR

This paper introduces a novel asymmetric quadratic metrics dual-front active contour model that improves image segmentation by reducing contour shortcut risks and effectively handling complex intensity distributions.

Contribution

It presents a new asymmetric dual-front model integrating image features and vector fields, enhancing segmentation accuracy especially in challenging scenarios.

Findings

Achieves encouraging segmentation results on synthetic and real images.

Reduces contour leakage and shortcut problems.

Compatible with various region-based homogeneity terms.

Abstract

The Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems. In the basic formulation of the dual-front models, the evolving contours can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a type of asymmetric quadratic metrics dual-front model. The metrics considered are built by the integration of the image features and a vector field derived from the evolving contours. The use of the asymmetry enhancement can reduce the risk of contour shortcut or leakage problems especially when the initial contours are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results.