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White Matter Lesion Segmentation from Volumetric MR Images

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Medical Imaging and Augmented Reality (MIAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3150))

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White matter lesions are common pathological findings in MR tomograms of elderly subjects. These lesions are typically caused by small vessel diseases (e.g., due to hypertension, diabetes). In this paper, we introduce an automatic algorithm for segmentation of white matter lesions from volumetric MR images. In the literature, there are methods based on multi-channel MR images, which obtain good results. But they assume that the different channel images have same resolution, which is often not available. Although our method is also based on T1 and T2 weighted MR images, we do not assume that they have the same resolution (Generally, the T2 volume has much less slices than the T1 volume). Our method can be summarized as the following three steps: 1) Register the T1 image volume and the T2 image volume to find the T1 slices corresponding to those in the T2 volume; 2) Based on the T1 and T2 image slices, lesions in these slices are segmented; 3) Use deformable models to segment lesion boundaries in those T1 slices, which do not have corresponding T2 slices. Experimental results demonstrate that our algorithm performs well.

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  1. Zijdenbos, P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric Analysis of White Matter Lesions in MR Images: Method and Validation. IEEE Trans. Med. Imag. 13, 716–724 (1994)

    Article  Google Scholar 

  2. Kapouleas, I.: Automatic Detection ofWhiteMatter Lesions inMagnetic Resonance Brain Images. Comput. Methods and programs in Biomed 32, 17–35 (1990)

    Article  Google Scholar 

  3. Leemput, K.V., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection. IEEE Trans. Med. Imag. 20, 677–688 (2001)

    Article  Google Scholar 

  4. Pachai, C., Zhu, Y.M., Grimaud, J., Hermier, M., Dromigny-Badin, A., Boudraa, A., Gimenez, G., Confavreux, C., Froment, J.C.: A Pyramidal Approach for Automatic Segmentation of Multiple Sclerosis Lesions in Brain MRI. Computerized Medical Imaging and Graphics 22, 399–408 (1998)

    Article  Google Scholar 

  5. Kovalev, V.A., Kruggel, F., Gertz, H.J., Cramon, D.Y.V.: Three-Dimensional Texture Analysis of MRI Brain Datasets. IEEE Trans. Med. Imag. 20, 424–433 (2001)

    Article  Google Scholar 

  6. Hojjatoleslami, S.A., Kruggel, F., von Cramon, D.Y.: Segmentation of White Matter Lesions from Volumetric MR images. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 52–61. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  7. Kamber, M., Shinghal, R., Collins, D.L., Francis, G.S., Evans, A.C.: Model- Based 3-D Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Brain Images. IEEE Trans. Med. Imag. 14, 442–453 (1995)

    Article  Google Scholar 

  8. Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality Image Registration by Maximization of Mutual Information. IEEE Trans. Med. Imag. 16, 187–198 (1997)

    Article  Google Scholar 

  9. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data. IEEE Trans. Med. Imag. 21, 193–199 (2002)

    Article  Google Scholar 

  10. Lobregt, S., Viergever, M.A.: A Discrete Dynamic Contour Model. IEEE Trans. Med. Imag. 14, 12–24 (1995)

    Article  Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Yang, F., Jiang, T., Zhu, W., Kruggel, F. (2004). White Matter Lesion Segmentation from Volumetric MR Images. In: Yang, GZ., Jiang, TZ. (eds) Medical Imaging and Augmented Reality. MIAR 2004. Lecture Notes in Computer Science, vol 3150. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22877-6

  • Online ISBN: 978-3-540-28626-4

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