Abstract
The inclusion of statistical region-based information in the Geodesic Active Contours introduces robustness in the segmentation of images with weak or inhomogeneous gradient at edges. The estimation of the Probability Density Function (PDF) for each region, involves the definition of the features that characterize the image inside the different regions. PDFs are usually modelled from the intensity values using Gaussian Mixture Models. However, we argue that the use of up to second order information could provide better discrimination of the different regions than based on intensity only, as the local intensity manifold is more accurately represented. In this paper, we present a non parametric estimation technique for the PDFs of the underlying tissues present in medical images with application for the segmentation of brain aneurysms in CTA data with the Geodesic Active Regions model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aylward, S.R., Bullitt, E.: Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction, Medical Imaging. IEEE Transactions on 21(2), 61–75 (2002)
Dasarathy, B.: Nearest neighbor pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1990)
Deschamps, T.: Curve and Shape Extraction with Minimal Path and Level-Sets techniques. Applications to 3D Medical Imaging, Ph.D. Thesis, University of Paris- Dauphine, France (December 2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, J. Wiley, Chichester (2001)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning. data mining, inference and prediction. Springer, Heidelberg (2001)
Hochmuth, A., Spetzger, U., Schumacher, M.: Comparison of three-dimensional rotational angiography with digital subtraction angiography in the assessment of ruptured cerebral aneurysms. AJNR Am J Neuroradiol 23(7), 1199–1205 (2002)
Holtzman-Gazit, M., Goldsher, D., Kimmel, R.: Hierarchical segmentation of thin structures in volumetric medical images. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 562–569. Springer, Heidelberg (2003)
Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model based detection of tubular structures in 3d images. Computer Vision and Image Understanding 80(2), 130–171 (2000)
Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33, 25–41 (2000)
Lorigo, L.M., Faugeras, O., Grimson, W.E.L., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.F.: Codimension-two geodesic active contours for the segmentation of tubular structures. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 2000, vol. 1, pp. 444–451 (2000)
Paragios, N.: Geodesic active regions and level set methods: Contributions and applications in artificial vision, Ph.D. Thesis, University of Nice Sophia-Antipolis, France (2000)
Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stats. 33, 1065–1976 (1962)
Vapnik, V.N.: Statistical learning theory. J. Wiley, Chichester (1998)
White, P.M., Teasdale, E.M., Wardlaw, J.M., Easton, V.: Intracranial aneurysms: CT angiography and MR angiography for detection prospective blinded comparison in a large patient cohort. Radiology 219(3), 739–749 (2001)
Yim, P.J., Cebral, J.R., Mullick, R., Marcos, H.B., Choyke, P.L.: Vessel surface reconstruction with a tubular deformable model, Medical Imaging. IEEE Transactions on 20(12), 1411–1421 (2001)
Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation, Pattern Analysis and Machine Intelligence. IEEE Transactions on 18(9), 884–900 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hernandez, M., Frangi, A.F. (2004). Geodesic Active Regions Using Non-parametric Statistical Regional Description and Their Application to Aneurysm Segmentation from CTA. In: Yang, GZ., Jiang, TZ. (eds) Medical Imaging and Augmented Reality. MIAR 2004. Lecture Notes in Computer Science, vol 3150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28626-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-28626-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22877-6
Online ISBN: 978-3-540-28626-4
eBook Packages: Springer Book Archive