Abstract
In this paper we describe the application of a novel statistical video-modeling scheme to sequences of multiple sclerosis (MS) images taken over time. The analysis of the image-sequence input as a single entity, as opposed to a sequence of separate frames, is a unique feature of the proposed framework. Coherent space-time regions in a four-dimensional feature space (intensity, position (x,y), and time) and corresponding coherent segments in the video content are extracted by unsupervised clustering via Gaussian mixture modeling (GMM). The Expectation-Maximization (EM) algorithm is used to determine the parameters of the model according to the maximum likelihood principle. MS lesions are automatically detected, segmented and tracked in time by context-based classification mechanisms. Qualitative and quantitative results of the proposed methodology are shown for a sequence of 24 T2-weighted MR images, which was acquired from a relapsing-remitting MS patient over a period of approximately a year. The validation of the framework was performed by a comparison to an expert radiologist’s manual delineation.
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
Greenspan, H., Goldberger, J., Mayer, A.: Probabilistic Space-Time Modeling via Piecewise GMM. IEEE Trans. Pattern Analysis and Machine Intelligence 26(3), 384–396 (2004)
Grossman, R.I., McGowan, J.C.: Perspectives on multiple sclerosis. American Journal of Neuroradiology 19, 1251–1265 (1998)
Noseworthy, J.H., Lucchinetti, C., Rodriguez, M., Weinshenker, B.G.: Multiple Sclerosis. The New England Journal of Medicine 343(13), 938–952 (2000)
Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. on Medical Imaging 20, 677–688 (2001)
Warfield, S., Dengler, J., Zaers, J., Guttmann, C.R.G., Wells, W.M., Ettinger, G.J., Hiller, J., Kikinis, R.: Automatic Identification of Grey Matter Structures from MRI to Improve the Segmentation of White Matter Lesions. Journal of Image Guided Surgery 1(6), 326–338 (1995)
Wei, X., Warfield, S.K., Zou, K.H., Wu, Y., Li, X., Guimond, A., Mugler III, J.P., Benson, R.R., Wolfson, L., Weiner, H.L., Guttmann, C.R.G.: Quantitative Analysis of MRI Signal Abnormalities of Brain White Matter with High Reproducibility and Accuracy. Journal of Magnetic Resonance Imaging 15, 203–209 (2002)
Kikinis, R., Guttmann, C.R.G., Metcalf, D., Wells, W.M., Ettinger, G.J., Weiner, H.L., Jolesz, F.A.: Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects. Journal of Magnetic Resonance Imaging 9(4), 519–530 (1999)
Gerig, G., Welti, D., Guttmann, C.R.G., Colchester, A.C.F., Székely, G.: Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data. Medical Image Analysis 4(1), 31–42 (2000)
Rey, D., Stoeckel, J., Malandain, G., Ayache, N.: A Spatio-temporal Model-based Statistical Approach to Detect Evolving Multiple Sclerosis Lesions. In: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), Kauia, Hawaii, USA, December 2001, pp. 105–112 (2001)
Rey, D., Subsol, G., Delingette, H., Ayache, N.: Automatic Detection and Segmentation of Evolving Processes in 3(d) Medical Images: Application to Multiple Sclerosis. Medical Image Analysis 6(4), 163–179 (2002)
Dempster, A., Laird, N., Rubin, D.: Maximum Likelihood from incomplete data via the EM algorithm. J. of Royal Statistical Society 39(1), 1–38 (1977)
Greenspan, H., Mayer, A., Shahar, A.: A Probabilistic Framework for the Spatio-Temporal Segmentation of Multiple Sclerosis Lesions in MR Images of the Brain. In: Proceedings of SPIE International Symposium on Medical Imaging, San Diego, USA, pp. 1551–1559 (2003)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunication. John Wiley and Sons, New York (1991)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Quering. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shahar, A., Greenspan, H. (2004). Probabilistic Spatial-Temporal Segmentation of Multiple Sclerosis Lesions. In: Sonka, M., Kakadiaris, I.A., Kybic, J. (eds) Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. MMBIA CVAMIA 2004 2004. Lecture Notes in Computer Science, vol 3117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27816-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-27816-0_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22675-8
Online ISBN: 978-3-540-27816-0
eBook Packages: Springer Book Archive