Deformable Model with a Complexity Independent from Image Resolution

TL;DR

This paper introduces a parametric deformable model that maintains consistent complexity regardless of image resolution, automatically adapts topology, and preserves shape quality through a novel metric-based sampling approach.

Contribution

The proposed model's complexity is independent of image resolution and automatically adapts topology, improving shape representation without increasing computational complexity.

Findings

Model maintains constant complexity across different resolutions

Automatic topology changes during evolution

Effective on biomedical and synthetic images

Abstract

We present a parametric deformable model which recovers image components with a complexity independent from the resolution of input images. The proposed model also automatically changes its topology and remains fully compatible with the general framework of deformable models. More precisely, the image space is equipped with a metric that expands salient image details according to their strength and their curvature. During the whole evolution of the model, the sampling of the contour is kept regular with respect to this metric. By this way, the vertex density is reduced along most parts of the curve while a high quality of shape representation is preserved. The complexity of the deformable model is thus improved and is no longer influenced by feature-preserving changes in the resolution of input images. Building the metric requires a prior estimation of contour curvature. It is obtained using a robust estimator which investigates the local variations in the orientation of image gradient. Experimental results on both computer generated and biomedical images are presented to illustrate the advantages of our approach.