Abstract
Automatic multiple sclerosis (MS) lesion segmentation in magnetic resonance imaging (MRI) is a challenging task due to the small size of the lesions, its heterogeneous shape and distribution, overlapping tissue intensity distributions, and the inherent artifacts of MRI. In this paper we propose a pipeline for MS lesion segmentation that combines prior knowledge and contextual information into a boosting classifier. The prior knowledge is introduced in terms of atlas distribution of the main brain tissues while the contextual information is based on a large set of features describing the spatial context in the lesion neighbourhood. Besides, we investigate the inclusion of a probability map describing the likelihood of a voxel to be an outlier, i.e. not being part of any healthy tissue. The experimental results, performed using a set of 30 MRI volumes of MS patients with very different lesion load, shows the feasibility of our approach. Besides, the results demonstrate the benefits of taking the outlier map into account.
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.
References
Cabezas, M., Oliver, A., et al.: A review of atlas-based segmentation for magnetic resonance brain images. Comput. Meth. Prog. Biomed. 104(3), e158–e177 (2011)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 38(2), 337–374 (2000)
Geremia, E., Clatz, O., et al.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2), 378–390 (2011)
Jenkinson, M., Beckmann, C.F., et al.: FSL. NeuroImage 62(2), 782–790 (2012)
Lladó, X., Oliver, A., et al.: Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches. Inform. Sciences 186(1), 164–185 (2012)
Morra, J., Tu, Z., et al.: Automatic segmentation of MS lesions using a contextual model for the MICCAI grand challenge. In: Grand Challenge Work.: Mult. Scler. Lesion Segm. Challenge, pp. 1–11 (2008)
Okuda, T., Korogi, Y., et al.: Brain lesion: when should fluid-attenuated inversion recovery sequences be used in MR evaluation? Radiology 212(3), 793–798 (1999)
Rueckert, D., Sonoda, L., et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imag. 18(8), 712–721 (1999)
Schmidt, P., Gaser, C., et al.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage 59(4), 3774–3783 (2012)
Shotton, J., Winn, J., et al.: Textonboost: multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comp. Vis. 81(1), 2–23 (2009)
Tustison, N., Avants, B., et al.: N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imag. 29(6), 1310–1320 (2010)
Vovk, U., Pernus, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imag. 26(3), 405–421 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cabezas, M., Oliver, A., Freixenet, J., Lladó, X. (2013). A Supervised Approach for Multiple Sclerosis Lesion Segmentation Using Context Features and an Outlier Map. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_93
Download citation
DOI: https://doi.org/10.1007/978-3-642-38628-2_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
eBook Packages: Computer ScienceComputer Science (R0)