Abstract
Multi-atlas based label fusion has shown great success in automatic and accurate medical image segmentation. However, most existing methods often equally and independently treat each voxel in labeling, and thus 1) find difficulty in discerning real and useful neighbors from those confusing ones in atlas images and 2) cannot use the structure information in images to be segmented. Motivated by these problems, in this paper, we propose a novel semi-supervised sparse method (SSSM) for multi-atlas label fusion. In the SSSM method, we first construct a unified graph with both labeled and unlabeled voxels and then use sparse representation to automatically determine the corresponding graph weights, which is followed by semi-supervised classification on the graph. Experimental results on segmenting brain anatomical structures in MR images show that our proposed method achieves not only improved accuracy but also more smooth segmented images, compared with conventional label fusion methods.
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References
Artaechevarria, X., Munoz-Barrutia, A., Solorzano, C.O.: Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Trans. Med. Imag. 28, 1266–1277 (2009)
Coupe, P., Manjon, J.V., Fonov, V., Pruessner, J.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. Neuroimage 54, 940–954 (2011)
Rousseau, F., Habas, P.A., Studholme, C.: A Supervised Patch-Based Approach for Human Brain Labeling. IEEE Trans. Med. Imag. 30, 1852–1862 (2011)
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med. Imag. 23, 903–921 (2004)
Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 129–136. Springer, Heidelberg (2010)
Wang, H., Suh, J.W., Das, S.: Regression-Based Label Fusion for Multi-Atlas Segmentation. In: 23th IEEE Conference on Computer Vision and Pattern Regonition, pp. 1113–1120. IEEE Press, New York (2011)
Wright, J., Ganesh, A., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)
SLEP: Sparse learning with efficient projections, http://www.public.asu.edu/~jye02
Yan, S., Wang, H.: Semi-supervised Learning by Sparse Representation. In: The SIAM Data Mining Conference, pp. 792–801. CSREA Press, Philadelphia (2009)
Christensen, G.E., Geng, X., Kuhl, J.G., Bruss, J., Grabowski, T.J., Pirwani, I.A., Vannier, M.W., Allen, J.S., Damasio, H.: Introduction to the Non-rigid Image Registration Evaluation Project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128–135. Springer, Heidelberg (2006)
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Guo, Q., Zhang, D. (2012). Semi-Supervised Sparse Label Fusion for Multi-atlas Based Segmentation. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_58
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DOI: https://doi.org/10.1007/978-3-642-33506-8_58
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