Abstract
We show a procedure for constructing a probabilistic atlas based on affine moment descriptors. It uses a normalization procedure over the labeled atlas. The proposed linear registration is defined by closed-form expressions involving only geometric moments. This procedure applies both to atlas construction as atlas-based segmentation. We model the likelihood term for each voxel and each label using parametric or nonparametric distributions and the prior term is determined by applying the vote-rule. The probabilistic atlas is built with the variability of our linear registration. We have two segmentation strategy: a) it applies the proposed affine registration to bring the target image into the coordinate frame of the atlas or b) the probabilistic atlas is non-rigidly aligning with the target image, where the probabilistic atlas is previously aligned to the target image with our affine registration. Finally, we adopt a graph cut - Bayesian framework for implementing the atlas-based segmentation.
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References
Cootes, T., Taylor, C., Cooper, D., Graham, J., et al.: Active shape models-their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Leventon, M., Grimson, W., Faugeras, O.: Statistical shape influence in geodesic active contours. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE Computer Society, Los Alamitos (1999/2000)
Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging 22, 137–154 (2003)
Cremers, D., Rousson, M.: Efficient kernel density estimation of shape and intensity priors for level set segmentation. In: Suri, J.S., Farag, A. (eds.) Parametric and Geometric Deformable Models: An application in Biomaterials and Medical Imagery. Springer, Heidelberg (2007)
Wang, Q., Seghers, D., D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P., Hammers, A.: Construction and validation of mean shape atlas templates for atlas-based brain image segmentation. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 689–700. Springer, Heidelberg (2005)
Park, H., Bland, P., Meyer, C.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on Medical Imaging 22, 483–492 (2003)
Greig, D., Porteous, B., Seheult, A.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society. Series B (Methodological) 51, 271–279 (1989)
Ishikawa, H.: Exact optimization for Markov random fields with convex priors. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1333–1336 (2003)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16, 187–198 (2002)
Heikkila, J.: Pattern matching with affine moment descriptors. Pattern Recognition 37, 1825–1834 (2004)
Pei, S., Lin, C.: Image normalization for pattern recognition. Image and Vision Computing 13, 711–723 (1995)
Saad, A., Hamarneh, G., Moller, T.: Exploration and Visualization of Segmentation Uncertainty Using Shape and Appearance Prior Information. IEEE Transactions on Visualization and Computer Graphics 16 (2010)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1222–1239 (2001)
Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.: elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 29 (2010)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)
van Ginneken, B., Heimann, T., Styner, M.: 3D segmentation in the clinic: A grand challenge. In: Proceedings of MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge, pp. 7–15 (2007)
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Platero, C., Rodrigo, V., Tobar, M.C., Sanguino, J., Velasco, O., Poncela, J.M. (2011). Probabilistic Atlas Based Segmentation Using Affine Moment Descriptors and Graph-Cuts. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_18
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DOI: https://doi.org/10.1007/978-3-642-23672-3_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23671-6
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