Abstract
Segmenting of Multiple Sclerosis (MS) lesions in magnetic resonance (MR) images is a hot issue in biomedical engineering. This paper presents a novel approach for segmentation of MS lesions in T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (Flair) MR images. The proposed approach is based on three-dimensional (3D) enhancement followed by false positive reduction methods and a three dimensional (3D) alpha matting technique. Firstly, the MS lesions in 3D volumes are enhanced driven by segmenting and enhancing single slices with MS lesions. Then a binary volume of interests (VOIs) of potential MS lesions is generated by thresholding. Secondly, multimodality information is used to segment the brain white matter. Then the location and the size of MS lesions are used to remove false positive VOIs. Finally, a 3D alpha matting method is utilized to refine the segmentation results, and to compute the VOIs with sub-pixel precision by considering partial volume effects. The experiments on real MRI data shows the unsupervised segmentation method can obtain better result than some state-of-the-art methods.
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
Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J.C., Quiles, A., Valls, L., Ramió, T.L., Rovira, A.: Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches. Information Sciences 186, 164–185 (2012)
Leemput, K.V., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Transactions on Medical Imaging 20(8), 677–688 (2001)
Dugas-Phocion, G., Gonzalez, M.A., Lebrun, C., Chanalet, S., Bensa, C., Malandain, G., Ayache, N.: Hierarchical segmentation of multiple sclerosis lesions in multi-sequence MRI. Biomedical Imaging: Nano to Macro (2004)
Aït-Ali, L.S., Prima, S., Hellier, P., Carsin, B., Edan, G., Barillot, C.: STREM: A robust multidimensional parametric method to segment MS lesions in MRI. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 409–416. Springer, Heidelberg (2005)
Zeng, Z., Zwiggelaar, R.: Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions. In: Gagalowicz, A., Philips, W. (eds.) MIRAGE 2011. LNCS, vol. 6930, pp. 133–144. Springer, Heidelberg (2011)
Souplet, J.C., Lebrun, C., Ayache, N., Malandain, G.: An automatic segmentation of T2-FLAIR multiple sclerosis lessions. In: The MIDAS Journal - MS lesion Segmentation (MICCAI 2008 Workshop), pp. 1–5 (2008)
Geremia, E., Menze, B.H., Clatz, O., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel MR images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 111–118. Springer, Heidelberg (2010)
Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)
Stephen, M.S.: Fast robust automated brain extraction. Human Brain Mapping 17(3), 143–155 (2002)
Kraskov, A., Stogbauer, H., Grassberger, P.: HMRF-EM-image: Estimating mutual information. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 69(6), 1–16 (2004)
Wang, W.H., Feng, Q.J., Chen, W.F.: Segmentation of brain MR images based on the measurement of difference of mutual information and Gauss-Markov random field model. Journal of Computer Research and Development 46(3), 521–527 (2009)
Prima, S., Ourselin, S., Ayache, N.: Computation of the mid-sagittal plane in 3-D brain images. IEEE Transactions on Medical Imaging 21(2), 122–138 (2002)
Grosman, R.I., Mcgowan, J.C.: Perspectives on Multiple Sclerosis. AJNR 19, 176–186 (1998)
Shao, H.C., Cheng, W.Y., Chen, Y.C.: Colored muti-neuron image processing for segmenting and tracing neural circuits. In: Proceedings of IEEE International Conference on Image Processing, pp. 1–4 (2012)
Levin, A., Lischinski, D., Weiss, Y.: A Closed Form Solution to Natural Image Matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 228–242 (2008)
Styner, M., Lee, J., Chin, B., Chin, M.S., Commowick, O., Tran, H.H., Jewells, V., Warfield, S.: 3D segmentation in the clinic: A grand challenge II: MS lesion segmentation. MIDAS Journal, 1–5 (2008)
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
Zeng, Z., Zwiggelaar, R. (2013). Segmentation for Multiple Sclerosis Lesions Based on 3D Volume Enhancement and 3D Alpha Matting. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-39094-4_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39093-7
Online ISBN: 978-3-642-39094-4
eBook Packages: Computer ScienceComputer Science (R0)