Abstract
We propose a method for the segmentation of Multiple Sclerosis lesions. The method is based on probability maps derived from a K-Nearest Neighbours classification. These are used as a non parametric likelihood in a Bayesian formulation with a prior that assumes connectivity of neighbouring voxels. The formulation is solved using the method of Iterated Conditional Modes (ICM). The parameters of the method are found through leave-one-out cross validation on training data after which it is evaluated on previously unseen test data. The multi modal features investigated are 3 structural MRI modalities, the diffusion MRI measures of Fractional Anisotropy (FA), Mean Diffusivity (MD) and several spatial features. Results show a benefit from the inclusion of diffusion primarily to the most difficult cases. Results shows that combining probabilistic K-Nearest Neighbour with a Markov Random Field formulation leads to a slight improvement of segmentations.
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References
Filippi, M., Horsfield, M.A., Morrissey, S.P., MacManus, D.G., Rudge, P., McDonald, W.I., Miller, D.H.: Quantitative brain MRI lesion load predicts the course of clinically isolated syndromes suggestive of multiple sclerosis. Neurology 44(4), 635–641 (1994)
Filippi, M., Cercignani, M., Inglese, M., Horsfield, M.A., Comi, G.: Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 56(3), 304–311 (2001)
Van Leemput, K., Maes, F., Vandermmeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2004)
Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)
Anbeek, P., Vincken, K.L., van Osch, M.J.P., Bisschops, R.H.C., van der Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21, 1037–1044 (2004)
Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistics Society B 48, 259–302 (1986)
Johnston, B., Atkins, M.S., Booth, K.S.: Partial volume segmentation in 3D of lesions and tissues in magnetic resonance images. In: Proceedings of SPIE: Medical Imaging 1994, vol. 2167, pp. 28–39 (1994)
Basser, P.J., Mattiello, J., Le Bihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994)
Dice, L.R.: Measures of the Amount of Ecologic Association Between Species. Ecology 26 (3), 297–302 (1945)
Reese, T.G., Heid, O., Weisskoff, R.M., Wedeen, V.J.: Reduction of eddy-current-induced distortion in diffusion mri using a twice-refocused spin echo. Magn. Reson. Med. 49, 177–182 (2003)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data. IEEE Trans. on Medical Imaging 17, 87–97 (1998)
Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated Multi-modality Image Registration Based On Information Theory. Medical Imaging, 263–274 (1995)
Jezzard, P., Balaban, R.S.: Correction for geometric distortion in echo planar images from Bo field variations. Magn. Reson. Med. 34(1), 65–73 (1995)
Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. on Medical Imaging 20(11), 1131–1139 (2001)
Cook, P.A., Bai, Y., Nedjati-Gilani, S., Seunarine, K.K., Hall, M.G., Parker, G.J., Alexander, D.C.: Camino: Open-Source Diffusion-MRI Reconstruction and Processing. In: 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, pp. 27–59 (2006)
Cover, T.M., Hart, P.E.: Nearest Neighbor Pattern Classification. IEEE Trans. on Information Theory 13, 21–27 (1967)
Muja, M., Lowe, D.G.: Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. In: Int. Conf. on Computer Vision Theory and Application, VISSAPP 2009, pp. 331–340 (2009)
Dyrby, T.B., Rostrup, E., Baare, F.C., Straaten, E.C.W., Barkhof, F., Vrenken, H., Ropele, S., Schmidt, R., Erkinjuntti, T., Wahlund, L.O., Pantoni, L., Inzitari, D., Paulson, O.B., Hansen, L.K., Waldemar, G.: Segmentation of age related white matter changes in a clinical multi-center study. NeuroImage 41, 335–345 (2008)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)
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Lyksborg, M. et al. (2012). Segmenting Multiple Sclerosis Lesions Using a Spatially Constrained K-Nearest Neighbour Approach. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_19
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DOI: https://doi.org/10.1007/978-3-642-31298-4_19
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