Elastic body spline based image segmentation

Abstract

Elastic body splines (EBS) belonging to the family of 3D splines were recently introduced to capture tissue deformations within a physical model-based approach for non-rigid biomedical image registration [1]. EBS model the displacement of points in a 3D homogeneous isotropic elastic body subject to forces. We propose a novel extension of using elastic body splines for learning driven figure-ground segmentation. The task of interactive image segmentation, with user provided foreground-background labeled seeds or samples, is formulated as learning an interpolating pixel classification function that is then used to assign labels for all unlabeled pixels in the image. The spline function we chose to model the supervised pixel classifier is the Gaussian elastic body spline (GEBS) which can use sparse scribbles from the user and has a closed form solution enabling a fast on-line implementation. Experimental results demonstrate the applicability of the GEBS approach for image segmentation. The GEBS method for interactive foreground image labeling shows promise and outperforms a previous approach using the thin-plate spline model.