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Machine Vision and Applications

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01.07.2013 | Original Paper

High-dimensional MRI data analysis using a large-scale manifold learning approach

verfasst von: Loc Tran, Debrup Banerjee, Jihong Wang, Ashok J. Kumar, Frederic McKenzie, Yaohang Li, Jiang Li

Erschienen in: Machine Vision and Applications | Ausgabe 5/2013

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Abstract

A novel manifold learning approach is presented to efficiently identify low-dimensional structures embedded in high-dimensional MRI data sets. These low-dimensional structures, known as manifolds, are used in this study for predicting brain tumor progression. The data sets consist of a series of high-dimensional MRI scans for four patients with tumor and progressed regions identified. We attempt to classify tumor, progressed and normal tissues in low-dimensional space. We also attempt to verify if a progression manifold exists—the bridge between tumor and normal manifolds. By identifying and mapping the bridge manifold back to MRI image space, this method has the potential to predict tumor progression. This could be greatly beneficial for patient management. Preliminary results have supported our hypothesis: normal and tumor manifolds are well separated in a low-dimensional space. Also, the progressed manifold is found to lie roughly between the normal and tumor manifolds.

Vorheriger Artikel Feature matching based on unsupervised manifold alignment

Nächster Artikel Detection of moving objects with a moving camera using non-panoramic background model

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Pauleit, D., Langen, K.-J., Floeth, F., Hautzel, H., Riemenschneider, M.J., Reifenberger, G., Shah, N.J., Muller, H.-W.: Can the apparent diffusion coefficient be used as a noninvasive parameter to distinguish tumor tissue from peritumoral tissue in cerebral gliomas? J. Magn. Reson. Imag. 20(5), 758–764 (2004)CrossRef

Bode, M.K., Ruohonen, J., Nieminen, M.T., Pyhtinen, J.: Potential of diffusion imaging in brain tumors: a review. Acta Radiol. 47(6), 585–594 (2006)CrossRef

Sinha, S., Bastin, M.E., Whittle, I.R., Wardlaw, J.M.: Diffusion tensor MR imaging of high-grade cerebral gliomas. Am. J. Neuroradiol. 23(4), 520–527 (2002)

Castillo, M., Smith, J.K., Kwock, L., Wilber, K.: Apparent diffusion coefficients in the evaluation of high-grade cerebral gliomas. Am. J. Neuroradiol. 22(1), 60–64 (2001)

Kono, K., Inoue, Y., Nakayama, K., Shakudo, M., Morino, M., Ohata, K., Wakasa, K., Yamada, R.: The role of diffusion-weighted imaging in patients with brain tumors. Am. J. Neuroradiol. 22(6), 1081–1088 (2001)

Bulakbasi, N., Kocaoglu, M., Ors, F., Tayfun, C., Ucoz, T.: Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. Am. J. Neuroradiol. 24(2), 225–233 (2003)

Lam, W., Poon, W., Metreweli, C.: Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma? Clin. Radiol. 57(3), 219–225 (2002)CrossRef

Yang, D., Korogi, Y., Sugahara, T., Kitajima, M., Shigematsu, Y., Liang, L., Ushio, Y., Takahashi, M.: Cerebral gliomas: prospective comparison of multivoxel 2D chemical-shift imaging proton mr spectroscopy, echoplanar perfusion and diffusion-weighted MRI. Neuroradiology 44, 656–666 (2002)CrossRef

Sadeghi, N., Camby, I., Goldman, S., Gabius, H.-J., Baleriaux, D., Salmon, I., Decaesteckere, C., Kiss, R., Metens, T.: Effect of hydrophilic components of the extracellular matrix on quantifiable diffusion-weighted imaging of human gliomas: preliminary results of correlating apparent diffusion coefficient values and hyaluronan expression level. Am. J. Roentgenol. 181(1), 235–241 (2003)CrossRef

10.

Bastin, M.E., Sinha, S., Whittle, I.R., Wardlaw, J.M.: Measurements of water diffusion and T1 values in peritumoural oedematous brain. Neuroreport 13(10), 1335–1340 (2002)CrossRef

11.

Lu, S., Ahn, D., Johnson, G., Law, M., Zagzag, D., Grossman, R.I.: Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. Radiology 232(1), 221–228 (2004)CrossRef

12.

Provenzale, J.M., McGraw, P., Mhatre, P., Guo, A.C., Delong, D.: Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. Radiology 232(2), 451–460 (2004)CrossRef

13.

Li, J., Wang, J., Shen, Y., Shen, Y., McKenzie, R., Guha-Thakurta, N.: Brain tumor progression assessment using multiple MRI volumes. In: Radiological society of North America (RSNA) 95th scientific assembly and annual meeting, oral presentation, Chicago, IL

14.

Shen, Y., Banerjee, D., Li, J., Chandler, A., Shen, Y., McKenzie, F., Wang, J.: Prediction of brain tumor progression using a machine learning technique. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 7624 (2010)

15.

Banerjee, D., Tran, L., Li, J., Shen, Y., McKenzie, F, Wang, J.: Prediction of brain tumor progression using multiple histogram matched MRI scans. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 7963, Provided by the SAO/NASA Astrophysics Data System (2011). doi:10.1117/12.878208. http://adsabs.harvard.edu/abs/2011SPIE.7963E..96B.

16.

Deshpande, A., Rademacher, L., Vempala, S., Wang, G.: Matrix approximation and projective clustering via volume sampling. In: Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm, SODA ’06, pp. 1117–1126. ACM, New York, NY, USA (2006)

17.

Drineas, P., Mahoney, M.W.: On the Nystrom method for approximating a Gram matrix for improved kernel-based learning. J. Mach. Learn. Res. 6, 2153–2175 (2005)MathSciNetMATH

18.

Wang, S., Yao, J., Summers, R.M.: Improved classifier for computer-aided polyp detection in CT colonography by nonlinear dimensionality reduction. Med. Phys. 35(4), 1377–1386 (2008)CrossRef

19.

de Silva, V., Tenenbaum, J.B.: Global versus local methods in nonlinear dimensionality reduction. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, pp. 705–712. MIT Press, Cambridge (2003)

20.

Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Sci. Comput. 26, 313–338 (2005)MathSciNetCrossRef

21.

Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRef

22.

Hartkens, T., Rueckert, D., Schnabel, J.A., Hawkes, D.J., Hill, D.L.G.: Vtk cisg registration toolkit: an open source software package for affine and nonrigid registration of single- and multimodal 3d images. In: Meiler, M., Saupe, D., Kruggel, F., Handels, H., Lehmann, T.M. (eds.) Bildverarbeitung fur die Medizin, vol. 56. CEUR Workshop Proceedings, pp. 409–412, Springer, Berlin (2002)

23.

Madabhushi, A., Udupa, J.K., Souza, A.: Generalized scale: theory, algorithms, and application to image inhomogeneity correction. Comput. Vis. Image Underst. 101, 100–121 (2006)CrossRef

24.

Nyul, L.G., Udupa, J.K.: On standardizing the MR image intensity scale. Magn. Reson. Med. 42(6), 1072–1081 (1999)CrossRef

25.

Wang, J., Zhang, Z., Zha, H.: Adaptive manifold learning. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1473–1480. MIT Press, Cambridge, MA (2005)

26.

Niskanen, M., Silven, O.: Comparison of dimensionality reduction methods for wood surface inspection. In: Proceedings of the 6th International Conference on Quality Control by Artificial Vision, pp. 178–188. Gatlinburg, TN, USA (2003)

27.

Lim, I.S., de Heras Ciechomski, P., Sarni, S., Thalmann, D.: Planar arrangement of high-dimensional biomedical data sets by isomap coordinates. In: Computer-Based Medical Systems, 2003. Proceedings. 16th IEEE, Symposium, pp. 50–55 (2003)

28.

Raytchev, B., Yoda, I., Sakaue, K.: Head pose estimation by nonlinear manifold learning. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 4, pp. 462–466 (2004)

29.

van der Maaten, L., Postma, E.O., van den Herik, H.J.: Dimensionality reduction: a comparative review (2008)

30.

Duda, R., Hart, P., Stork, D.: Pattern classification and scene analysis: pattern classification. Wiley, New York (2001)

31.

Bishop, C.: Pattern recognition and machine Learning. Information science and statistics. Springer, Berlin (2006)

Titel: High-dimensional MRI data analysis using a large-scale manifold learning approach
verfasst von: Loc Tran
Debrup Banerjee
Jihong Wang
Ashok J. Kumar
Frederic McKenzie
Yaohang Li
Jiang Li
Publikationsdatum: 01.07.2013
Verlag: Springer-Verlag
Erschienen in: Machine Vision and Applications / Ausgabe 5/2013
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-013-0499-8

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