A Geodesic Voting Shape Prior to Constrain the Level Set Evolution for the Segmentation of Tubular Trees
This paper presents a geodesic voting method to segment tree structures, such as retinal or cardiac blood vessels. Many authors have used minimal cost paths, or similarly geodesics relative to a weight potential P, to find a vessel between two end points. Our goal focuses on the use of a set of such geodesic paths for finding a tubular tree structures, using minimal interaction. This work adapts the geodesic voting method that we have introduced for the segmentation of thin tree structures to the segmentation of tubular trees. The original approach of geodesic voting consists in computing geodesics from a set of end points scattered in the image to a given source point. The target structure corresponds to image points with a high geodesic density. Since the potential takes low values on the tree structure, geodesics will locate preferably on this structure and thus the geodesic density should be high. Geodesic voting method gives a good approximation of the localization of the tree branches, but it does not allow to extract the tubular aspect of the tree. Here, we use the geodesic voting method to build a shape prior to constrain the level set evolution in order to segment the boundary of the tubular structure. We show results of the segmentation with this approach on 2D angiogram images and 3D simulated data.
KeywordsTree Structure Segmentation Result Active Contour Minimal Path Active Contour Model
Unable to display preview. Download preview PDF.
- 12.Cohen, L.D., Deschamps, T.: Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging. Computer Methods in Biomechanics and Biomedical Engineering 10(4) (2007)Google Scholar
- 13.Rouchdy, Y., Cohen, L.D.: Image segmentation by geodesic voting. application to the extraction of tree structures from confocal microscope images. In: The 19th International Conference on Pattern Recognition, Tampa, Florida, pp. 1–5 (2008)Google Scholar
- 14.Rouchdy, Y., Cohen, L.D.: The shading zone problem in geodesic voting and its solutions for the segmentation of tree structures. application to the segmentation of microglia extensions. In: Computer Vision and Pattern Recognition Workshop MMBIA, Miami, Florida, pp. 66–71 (2009)Google Scholar
- 19.Leventon, M.E., Faugeras, O.D., Grimson, W.E.L., Wells III, W.E.: Level set based segmentation with intensity and curvature prior. In: MMBIA, pp. 4–11 (2000)Google Scholar
- 22.Hameeteman, K., Freiman, M., Zuluaga, M.A., Joskowicz, L., Rozie, S., van Gils, M.J., van den Borne, L., Sosna, J., Berman, P., Cohen, N., Douek, P., Snchez, I., Aissat, M., van der Lugt, A., Krestin, G.P., Niessen, W.J., van Walsum, T.: Carotid lumen segmentation and stenosis grading challenge. In: MICCAI 2009 (2009)Google Scholar