We present an automatic three-dimensional segmentation approach based on continuous max flow that targets tubular structures in medical images. Our method uses second-order derivative information provided by Frangi et al.’s vesselness feature and exploits it twofold: First, the vesselness response itself is used for localizing the tubular structure of interest. Second, the eigenvectors of the Hessian eigendecomposition guide our anisotropic total variation–regularized segmentation. In a simulation experiment, we demonstrate the superiority of anisotropic as compared to isotropic total variation–regularized segmentation in the presence of noise. In an experiment with magnetic resonance images of the human cervical spinal cord, we compare our automated segmentations to those of two human observers. Finally, a comparison with a dedicated state-of-the-art spinal cord segmentation framework shows that we achieve comparable to superior segmentation quality.

In this paper, we address the problem of segmenting data defined on a manifold into a set of regions with uniform properties. In particular, we propose a numerical method when the manifold is represented by a triangular mesh. Based on recent image segmentation models, our method minimizes a convex energy and then enjoys significant favorable properties: it is robust to initialization and avoid the problem of the existence of local minima present in many variational models. The contributions of this paper are threefold: firstly we adapt the convex image labeling model to manifolds; in particular the total variation formulation. Secondly we show how to implement the proposed method on triangular meshes, and finally we show how to use and combine the method in other computer vision problems, such as 3D reconstruction. We demonstrate the efficiency of our method by testing it on various data.

In this paper we propose a novel prior-based variational object segmentation method in a global minimization framework which unifies image segmentation and image denoising. The idea of the proposed method is to convexify the energy functional of the Chan-Vese method in order to find a global minimizer, so called continuous graph cuts. The method is extended by adding an additional shape constraint into the convex energy functional in order to segment an object using prior information. We show that the energy functional including a shape prior term can be relaxed from optimization over characteristic functions to optimization over arbitrary functions followed by a thresholding at an arbitrarily chosen level between 0 and 1. Experimental results demonstrate the performance and robustness of the method to segment objects in real images.

In this paper we propose a new method for variational segmentation of image sequences containing nonrigid, moving objects. The method is based on the Chan-Vese model augmented with a novel frame-to-frame interaction term, which allow us to update the segmentation result from one image frame to the next using the previous segmentation result as a shape prior. The interaction term is constructed to be pose-invariant and to allow moderate deformations in shape. It can handle the appearance of occlusions which otherwise can make segmentation fail. The performance of the model is illustrated with experiments on synthetic and real image sequences.