Showing papers by "Ben Glocker published in 2007"

Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming

Abstract: In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain. Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.

Primal/dual linear programming and statistical atlases for cartilage segmentation

Abstract: In this paper we propose a novel approach for automatic segmentation of cartilage using a statistical atlas and efficient primal/dual linear programming. To this end, a novel statistical atlas construction is considered from registered training examples. Segmentation is then solved through registration which aims at deforming the atlas such that the conditional posterior of the learned (atlas) density is maximized with respect to the image. Such a task is reformulated using a discrete set of deformations and segmentation becomes equivalent to finding the set of local deformations which optimally match the model to the image. We evaluate our method on 56 MRI data sets (28 used for the model and 28 used for evaluation) and obtain a fully automatic segmentation of patella cartilage volume with an overlap ratio of 0.84 with a sensitivity and specificity of 94.06% and 99.92%, respectively.

Towards a computer-aided diagnosis system for colon motility dysfunctions

Abstract: Colon motility disorders are a very common problem. A precise diagnosis with current methods is almost unachievable. This makes it extremely difficult for the clinical experts to decide for the right intervention such as colon resection. The use of cine MRI for visualizing the colon motility is a very promising technique. In addition, if image segmentation and qualitative motion analysis provide the necessary tools, it could provide the appropriate diagnostic solution. In this work we defined necessary steps in the image processing workflow to gain valuable measurements for a computer aided diagnosis of colon motility disorders. For each step, we developed methods to deal with the dynamic image data. There is need for compensating the breathing motion since no respiratory gating could be used. We segment the colon using a graph cuts approach in 2D and 3D for further analysis and visualization. The analysis of the large bowel motility is done by tracking the extension of the colon during a propagating peristaltic wave. The main objective of this work is to extract a motion model to define a clinical index that can be used in diagnosis of large bowel motility dysfunction. We aim at the classification and localization of such pathologies.

4D ventricular segmentation and wall motion estimation using efficient discrete optimization

Abstract: In this paper we propose a novel approach to ventricular motion estimation and segmentation. Our method is based on a MRF formulation where an optimal intensity-based separation between the endocardium and the rest of the cardiac volume is to be determined. Such a term is defined in the spatiotemporal domain, where the ventricular wall motion is introduced to account for correspondences between the consecutive segmentation maps. The estimation of the deformations is done through a continuous deformation field (FFD) where the displacements of the control points are determined using discrete labeling approach. Principles from linear programming and in particular the Primal/Dual Schema is used to recover the optimal solution in both spaces. Promising experimental results obtained on 13 MR spatiotemporal data sets demonstrate the potentials of our method.