Abstract
We present a new method for detecting the interface, or edge, structure present in diffusion MRI. Interface detection is an important first step for applications including segmentation and registration. Additionally, due to the higher dimensionality of tensor data, humans are visually unable to detect edges as easily as in scalar data, so edge detection has potential applications in diffusion tensor visualization. Our method employs the computer vision techniques of local structure filtering and normalized convolution. We detect the edges in the tensor field by calculating a generalized local structure tensor, based on the sum of the outer products of the gradients of the tensor components. The local structure tensor provides a rotationally invariant description of edge orientation, and its shape after local averaging describes the type of edge. We demonstrate the ability to detect not only edges caused by differences in tensor magnitude, but also edges between regions of different tensor shape. We demonstrate the method’s performance on synthetic data, on major fiber tract boundaries, and in one gray matter region.
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Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT–MRI data. Magnetic Resonance in Medicine 44, 625–632 (2000)
Behrens, T.E.J., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A.M., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Ciccarelli, O., Thompson, A.J., Brady, J.M., Matthews, P.M.: Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience 6, 750–757 (2003)
Bigün, J., Granlund, G.H., Wiklund, J.: Multidimensional orientation: texture analysis and optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI–13(8) (August 1991)
Förstner, W.: A feature based correspondence algorithm for image matching. Int. Arch. Photogrammetry Remote Sensing 26(3), 150–166 (1986)
Feddern, C., Weickert, J., Burgeth, B.: Level-set methods for tensor-valued images. In: Faugeras, O., Paragios, N. (eds.) Proc. Second IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision, pp. 65–72 (2003)
Gudbjartsson, H., Maier, S., Mulkern, R., Morocz, I.A., Patz, S., Jolesz, F.: Line scan diffusion imaging. Magnetic Resonance in Medicine 36, 509–519 (1996)
Knutsson, H.: Representing local structure using tensors. In: The 6th Scandinavian Conference on Image Analysis, Oulu, Finland, June 1989, pp. 244–251 (1989)
Knutsson, H., Bårman, H., Haglund, L.: Robust orientation estimation in 2D, 3D and 4D using tensors. In: Proceedings of Second International Conference on Automation, Robotics and Computer Vision, ICARCV1992, Singapore (September 1992)
Knutsson, H., Westin, C.-F.: Normalized and differential convolution: Methods for interpolation and filtering of incomplete and uncertain data. Computer Vision and Pattern Recognition, 515–523 (1993)
Koller, T.M., Gerig, G., Szekely, G., Dettwiler, D.: Multiscale detection of curvilinear structures in 2D and 3D image data. In: Proc. ICCV 1995, pp. 864–869 (1995)
Rodriguez-Florido, M.A., Westin, C.-F., Ruiz-Alzola, J.: Dt-mri regularization using anisotropic tensor field filtering. In: IEEE International Symposium on Biomedical Imaging, vol. 3363, pp. 336–339 (2004)
Deriche, R., Monga, O., Lengagne, R.: Extraction of zero crossings of the curvature derivatives in volumetric 3D medical images: a multi-scale approach. In: Proc. IEEE Conf. Comp. Vision and Pattern Recognition, Seattle, Washington, USA, June 1994, pp. 852–855 (1994)
Rohr, K.: Extraction of 3D anatomical point landmarks based on invariance principles. Pattern Recognition 32, 3–15 (1999)
Ruiz-Alzola, J., Kikinis, R., Westin, C.-F.: Detection of point landmarks in multidimensional tensor data. Signal Processing 81, 2243–2247 (2001)
Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: Three-dimensional multiscale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis 2(2), 143–168 (1998)
Tuch, D.S.: Diffusion MRI of Complex Tissue Structure. PhD thesis, Division of Health Sciences and Technology, Massachusetts Institute of Technology (2002)
Westin, C.-F., Maier, S.E., Mamata, H., Nabavi, A., Jolesz, F.A., Kikinis, R.: Processing and visualization of diffusion tensor MRI. Medical Image Analysis 6(2), 93–108 (2002)
Westin, C.-F., Richolt, J., Moharir, V., Kikinis, R.: Affine adaptive filtering of CT data. Medical Image Analysis 4(2), 161–172 (2000)
Westin, C.-F.: Multidimensional Signal Processing. PhD thesis, Linkoping University (1994)
Wiegell, M.R., Tuch, D.S., Larson, H.W.B., Wedeen, V.J.: Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. Neuroimage 19, 391–402 (2003)
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O’Donnell, L., Grimson, W.E.L., Westin, CF. (2004). Interface Detection in Diffusion Tensor MRI. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_44
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DOI: https://doi.org/10.1007/978-3-540-30135-6_44
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