Abstract
Abnormalities in the white matter of the brain are common to subjects with multiple sclerosis and Alzheimer’s disease. They also develop in normal, asymptomatic, subjects and appear more frequently with age. Clinically, it is interesting to be able to differentiate between different disease states and to find markers which allow early diagnosis. Conventional spin echo (CSE) magnetic resonance imaging (MRI) is sensitive to these white matter changes and has frequently been applied to their study.
Previous approaches to investigate white matter abnormalities have often been reported to have difficulty distinguishing between normal gray matter and abnormal white matter due to their similar appearance in MRI. Earlier methods have also often generated binary classifications, reporting white matter as either normal or abnormal.
We have developed a new approach which first identifies the region of white matter using a template moderated spatially varying classification, and then estimates the degree of white matter abnormality present at each voxel of the white matter. This fractional segmentation allows us to preserve the heterogeneous characteristics of white matter abnormalities and to investigate both focal and diffuse white matter damage. We compute, from the fractional segmentation, a white matter spectrum showing the different levels of white matter damage present in each subject.
We applied this automated image segmentation method to over 996 MRI scans of subjects affected by multiple sclerosis, 72 normal aging subjects and 29 scans of subjects with Alzheimer’s disease. We investigated the ability to characterize these different subject groups based upon tissue volumes determined by spatially varying classification, and by the fractional segmentation of the white matter of each patient.
Chapter PDF
Similar content being viewed by others
References
Guttmann, C.R.G., Jolesz, F.A., Kikinis, R., Killiany, R.J., Moss, M.B., Sandor, T., Albert, M.S.: White matter changes with normal aging. Neurology 50, 972–978 (1998)
Grossman, R.I., McGowan, J.C.: Perspectives on Multiple Sclerosis. American Journal of Neuroradiology 19, 1251–1265 (1998)
Choi, H.S., Haynor, D.R., Kim, Y.: Partial Volume Tissue Classification of Multichannel Magnetic Resonance Images — A Mixel Model. IEEE Transactions On Medical Imaging 10(3), 395–407 (1991)
Bonar, D.C., Schaper, K.A., Anderson, J.R., Rottenberg, D.A., Strother, S.C.: Graphical Analysis of MR Feature Space for Measurement of CSF, Gray-Matter, and White-Matter Volumes. Journal of Computer Assisted Tomography 17(3), 461–470 (1993)
Kao, Y.-H., Sorenson, J.A., Bahn, M.M., Winkler, S.S.: Dual- Echo MRI Segmentation Using Vector Decomposition and Probability Techniques: A Two-Tissue Model. Magnetic Resonance in Medicine 32(3), 342–357 (1994)
Donald Gage, H., Santago, P., Snyder, W.E.: Quantification of Brain Tissue Through Incorporation of Partial Volume Effects. SPIE Medical Imaging VI: Image Processing 1652, 84–96 (1992)
Ross Mitchell, J., Jones, C., Karlik, S.J., Kennedy, K., Lee, D.H., Rutt, B., Fenster, A.: MR Multispectral Analysis of Multiple Sclerosis Lesions. JMRI 7, 499–511 (1997)
Guttmann, C.R.G., Ahn, S.S., Hsu, L., Kikinis, R., Jolesz, F.A.: The Evolution of Multiple Sclerosis Lesions on Serial MR. AJNR 16, 1481–1491 (1995)
Filippi, M., Horsfield, M.A., Tofts, P.S., Barkhof, F., Thompson, A.J., Miller, D.H.: Quantitative assessment of MRI lesion load in monitoring the evolution of multiple sclerosis. Brain 118, 1601–1612 (1995)
Wells, W.M., Kikinis, R., Grimson, W.E.L., Jolesz, F.: Adaptive segmentation of MRI data. IEEE Transactions On Medical Imaging 15, 429–442 (1996)
Kikinis, R., Shenton, M.E., Gerig, G., Martin, J., Anderson, M., Metcalf, D., Guttmann, C.R.G., McCarley, R.W., Lorenson, W.E., Cline, H., Jolesz, F.: Routine Quantitative Analysis of Brain and Cerebrospinal Fluid Spaces with MR Imaging. Journal of Magnetic Resonance Imaging 2, 619–629 (1992)
Warfield, S.K., Jolesz, F., Kikinis, R.: A High Performance Computing Approach to the Registration of Medical Imaging Data. Parallel Computing 24(9–10), 1345–1368 (1998)
Warfield, S.K., Robatino, A., Dengler, J., Jolesz, F.A., Kikinis, R.: Nonlinear Registration and Template Driven Segmentation. In: ch. 4, pp. 67–84, Progressive Publishing Alternatives (1998)
Warfield, S.K., Kaus, M., Jolesz, F.A., Kikinis, R.: Adaptive Template Moderated Spatially Varying Statistical Classification. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 231–238. Springer, Heidelberg (1998)
Warfield, S., Dengler, J., Zaers, J., Guttmann, C.R.G., Wells III, 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)
Iosifescu, D.V., Shenton, M.E., Warfield, S.K., Kikinis, R., Dengler, J., Jolesz, F.A., McCarley, R.W.: An Automated Registration Algorithm for Measuring MRI Subcortical Brain Structures. NeuroImage 6, 12–25 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Warfield, S.K., Westin, CF., Guttmann, C.R.G., Albert, M., Jolesz, F.A., Kikinis, R. (1999). Fractional Segmentation of White Matter. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_7
Download citation
DOI: https://doi.org/10.1007/10704282_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66503-8
Online ISBN: 978-3-540-48232-1
eBook Packages: Springer Book Archive