Abstract
The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures. We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset. The promising results of the segmentation method show the efficiency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
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Babalola, K., Patenaude, B., Aljabar, P., Schnabel, J., Kennedy, D., Crum, W., Smith, S., Cootes, T., Jenkinson, M., Rueckert, D.: An evaluation of four automatic methods of segmenting the subcortical structures in the brain. Neuroimage 47(4) (2009)
Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage 46(3), 726–738 (2009)
Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: Fully bayesian joint model for MR brain scan tissue, structure segmentation. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1066–1074. Springer, Heidelberg (2008)
Wolz, R., Aljabar, P., Rueckert, D., Heckemann, R., Hammers, A.: Segmentation of subcortical structures and the hippocampus in brain mri using graph-cuts and subject-specific a-priori information. IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 470–473 (2009)
Rousseau, F., Habas, P., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE Trans. on MIÂ 30(10) (2011)
Coupé, P., Manjón, J., Fonov, V., Pruessner, J., Robles, M., Collins, D.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. Neuroimage 54(2), 940–954 (2011)
Wang, H., Yushkevich, P.: Dependency prior for multi-atlas label fusion. In: ISBI: From Nano to Macro (ISBI), pp. 892–895 (2012)
Tong, T., Wolz, R., Coupé, P., Hajnal, J.V., Rueckert, D.: Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling. Neuroimage 76, 11–23 (2013)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. on IPÂ 15(12) (2006)
Bryt, O., Elad, M.: Compression of facial images using the K-SVD algorithm. IEEE Trans. on IPÂ 19(4) (2008)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 267–288 (1996)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B 67(2), 301–320 (2005)
Engan, K., Aase, S.O., Husoy, J.H.: Frame based signal compression using method of optimal directions (MOD). IEEE Intern. Symp. Circ. Syst. (1999)
Aharon, M., Elad, M., Bruckstein, A.M.: The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. SPÂ 54(11) (2006)
Zhang, Q., Li, B.: Discriminative K-SVD for dictionary learning in face recognition. In: CVPR, pp. 2691–2698 (2010)
Jiang, Z., Lin, Z., Davis, L.: Learning a discriminative dictionary for sparse coding via label consistent k-svd. In: CVPR, pp. 1697–1704 (2011)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online Dictionary Learning for Sparse Coding. In: International Conference on Machine Learning, Montreal, Canada (2009)
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Benkarim, O.M., Radeva, P., Igual, L. (2014). Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation. In: Perales, F.J., Santos-Victor, J. (eds) Articulated Motion and Deformable Objects. AMDO 2014. Lecture Notes in Computer Science, vol 8563. Springer, Cham. https://doi.org/10.1007/978-3-319-08849-5_14
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DOI: https://doi.org/10.1007/978-3-319-08849-5_14
Publisher Name: Springer, Cham
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