Abstract
Segmentation of low contrast objects is an important task in clinical applications like lesion analysis and vascular wall remodeling analysis. Several solutions to low contrast segmentation that exploit high-level information have been previously proposed, such as shape priors and generative models. In this work, we incorporate a priori distributions of intensity and low-level image information into a nonparametric dissimilarity measure that defines a local indicator function for the likelihood of belonging to a foreground object. We then integrate the indicator function into a level set formulation for segmenting low contrast structures. We apply the technique to the clinical problem of positive remodeling of the vessel wall in cardiac CT angiography images. We present results on a dataset of twenty five patient scans, showing improvement over conventional gradient-based level sets.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)
Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. In: Proceedings of the National Academy of Sciences, pp. 1591–1595 (1996)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(2), 158–175 (1995)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)
Leventon, M., Faugeras, O., Grimson, W.: Level set based segmentation with intensity and curvature priors. In: Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 4–11 (2000)
Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. International Journal of Computer Vision 46(3), 223–247 (2002)
Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42(5), 577–685 (1989)
Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (1999)
Baillard, C., Barillot, C.: Robust 3D segmentation of anatomical structures with level sets. In: Medical Image Computing and Computer-assisted Intervention, pp. 236–245 (2000)
Rathi, Y., Michailovich, O., Malcolm, J., Tannenbaum, A.: Seeing the unseen: Segmenting with distributions. In: International Conference on Signal and Image Processing (2006)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
Levina, E., Bickel, P.: The earth mover’s distance is the Mallows distance: some insights from statistics. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 251–256 (2001)
Glagov, S., Weisenberg, E., Zarins, C.K., Stankunavicius, R., Kolettis, G.J.: Compensatory enlargement of human atherosclerotic coronary arteries. New England Journal of Medicine 316(22), 1371–1375 (1987)
Crawford, M., DiMarco, J.P., Paulus, W.J. (eds.): Cardiology. Elsevier, Amsterdam (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Makrogiannis, S., Bhotika, R., Miller, J.V., Skinner, J., Vass, M. (2009). Nonparametric Intensity Priors for Level Set Segmentation of Low Contrast Structures. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-04268-3_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04267-6
Online ISBN: 978-3-642-04268-3
eBook Packages: Computer ScienceComputer Science (R0)