MM-DTI: Visualization and segmentation tool for diffusion tensor images
Abstract
In this paper we present the MM-DTI, a tool that offers diffusion tensor processing functionalities based on mathematical morphology operators. Through a graphic user interface, it is possible to configure and visualize a diffusion tensor image, to compute its tensorial morphological gradient (TMG) and finally, to segment it using the watershed transform. It offers three different visualization modes (tensor glyphs, volume rendering and tracts), three color mappings (FA-based, TMG-based and Segmentation-based) and an extra feature which provides an animation of the movement of water molecules through the interpolation of the objects position on the grid in the orientation and direction of the tensors principal eigenvector.References
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Pierpaoli, C. and Basser, P. (1996). Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Medic., 36(6):893–906.
Rittner, L. and Lotufo, R. (2008). Diffusion tensor imaging segmentation by watershed transform on tensorial morphological gradient. Brazilian Symp. on Computer Graph. and Image Proc., pages 196–203.
Symms, M., Jager, H. R., Schmierer, K., and Yousry, T. A. (2004). A review of structural magnetic resonance neuroimaging. J Neurol Neurosurg Psychiatry, 75(9):1235–1244.
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Weldeselassie, Y. and Hamarneh, G. (2007). DT-MRI segmentation using graph cuts. In Medical Imaging 2007: Image Processing. SPIE.
Westin, C.-F., Maier, S., Mamata, H., Nabavi, A., Jolesz, F., and Kikinis, R. (2002). Processing and visualization for diffusion tensor mri. Medical Image Analysis, 6(93-108).
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Assaf, B., Mohamed, F., Abou-Khaled, K., Williams, J., Yazeji, M., Haselgrove, J., and Faro, S. (2003). Diffusion Tensor Imaging of the Hippocampal Formation in Temporal Lobe Epilepsy. AJNR Am J Neuroradiol, 24(9):1857–1862.
Awate, S. and Gee, J. (2007). A fuzzy, nonparametric segmentation framework for DTI and MRI analysis. In IPMI, pages 296–307.
Basser, P. and Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor mri. J. Magn. Reson., 111(3):209–219.
Eriksson, S. H., Rugg-Gunn, F. J., Symms, M. R., Barker, G. J., and Duncan, J. S. (2001). Diffusion tensor imaging in patients with epilepsy and malformations of cortical development. Brain, 124(3):617–626.
Fillard, P. (2005). DTI-track. [link].
Fillard, P., Souplet, J., and Toussaint, N. (2009). MedINRIA. [link].
He, R., Mehta, M., and Narayana, P. (2004). Color coding for visualization of the directional information of dti. In IEMBS ’04. 26th Annual International Conference of the IEEE, volume 1, pages 1857–1859.
Jiang, H., van Zijl, P. C., Kim, J., Pearlson, G. D., and Mori, S. (2006). DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed, 81(2):106–116.
Lobo, R., Rittner, L., Lotufo, R., and Magalhes, L. (2009). MM-DTI. [link].
Pajevic, S. and Pierpaoli, C. (1999). Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: Application to white matter fiber tract mapping in the human brain. Magn. Reson. Med., 42:526–540.
Park, H. J., Kubicki, M., Westin, C.-F., Talos, I. F., Brun, A., Pieper, S., Kikinis, R., Jolesz, F. A., McCarley, R. W., and Shenton, M. E. (2004). Method for combining information from white matter fiber tracking and gray matter parcellation. American Journal of Neuroradiology, 25:1318–1324.
Pierpaoli, C. and Basser, P. (1996). Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Medic., 36(6):893–906.
Rittner, L. and Lotufo, R. (2008). Diffusion tensor imaging segmentation by watershed transform on tensorial morphological gradient. Brazilian Symp. on Computer Graph. and Image Proc., pages 196–203.
Symms, M., Jager, H. R., Schmierer, K., and Yousry, T. A. (2004). A review of structural magnetic resonance neuroimaging. J Neurol Neurosurg Psychiatry, 75(9):1235–1244.
Vilanova, A., Zhang, S., Kindlmann, G., and Laidlaw, D. H. (2005). An introduction to visualization of diffusion tensor imaging and its applications. In Visualization and Image Processing of Tensor Fields. Springer-Verlag.
Wang, Z. and Vemuri, B. (2005). DTI segmentation using an information theoretic tensor dissimilarity measure. IEEE Trans. Med. Imag.
Weldeselassie, Y. and Hamarneh, G. (2007). DT-MRI segmentation using graph cuts. In Medical Imaging 2007: Image Processing. SPIE.
Westin, C.-F., Maier, S., Mamata, H., Nabavi, A., Jolesz, F., and Kikinis, R. (2002). Processing and visualization for diffusion tensor mri. Medical Image Analysis, 6(93-108).
Zhang, S., Kindlmann, G., and Laidlaw, D. (2004). Diffusion tensor MRI visualization. In Visualization Handbook. Academic Press.
Published
2010-07-20
How to Cite
LOBO, Renan R.S.; RITTNER, Leticia; LOTUFO, Roberto A.; MAGALHÃES, Léo P..
MM-DTI: Visualization and segmentation tool for diffusion tensor images. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 10. , 2010, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2010
.
p. 1580-1589.
ISSN 2763-8952.
