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Semi-Automatic Segmentation of Ct/Mri Images Based on Active Contour Method for 3D Reconstruction of Abdominal Aortic Aneurysms

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Języki publikacji
EN
Abstrakty
EN
The paper presents a CT/MRI image based semi-automatic AAA (abdominal aortic aneurysm) segmentation method. Segmentation process can run automatically with the active contour method but results are controlled by the operator. If incorrect segmentation is noticed, the operator may introduce corrections. The proposed method makes possible the segmentation of dissected aneurysms, with which no automatic analysis works. Controlling the segmentation process by the operator serves to ensure correct geometric shape reproduction, which is crucial in deploying aneurysm models to help assess rupture risk.
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Twórcy
  • Poznan University of Technology, Institute of Control and Information Engineering ul. Piotrowo 3a 60-965 Poznan, Poland
autor
  • Poznan University of Technology, Institute of Control and Information Engineering ul. Piotrowo 3a 60-965 Poznan, Poland
Bibliografia
  • [1] Majewski, S. (1995). Histologia. Uklad kra˛z˙enia. Warszawa: Wydawnictwo Lekarskie PZWL.
  • [2] De Bruijne, M., Van Ginneken, B., Niessen, W. J., Loog, M., Viergever, M. A., (2003). Modelbased segmentation of abdominal aortic aneurysms in CTA images. Medical Imaging 2003, 1560-1571.
  • [3] M de Bruijne, B van Ginneken, M A Viergever, W J Niessen, (2004). Interactive segmentation of abdominal aortic aneurysms in CTA images. Medical Image Analysis, 8(2), 127-138.
  • [4] Olabarriaga, S. D., Rouet, J. M., Fradkin, M., Breeuwer, M., Niessen, W. J. (2005). Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling. IEEE Transactions on Medical Imaging, 24(4), 477-485.
  • [5] Auer, M., Gasser, T. C. (2010). Reconstruction and Finite Element Mesh Generation of Abdominal Aortic Aneurysms From Computerized Tomography Angiography Data With Minimal User Interactions. IEEE Transactions On Medical Imaging, 29(4), 1022-1028.
  • [6] Shum, J., DiMartino, E. S., Goldhammer, A., Goldman, D. H., Acker, L. C., Patel, G., . . . Finol, E. A. (2010). Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms. Medical Physics, 37(2), 638-649.
  • [7] Kass, M., Witkin, A., Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1, 321-331.
  • [8] Wang, H., Ghosh, B. (2000). Geometric active deformable models in shape modelling. IEEE Transactions on Image Processing, 9(2), 302-308.
  • [9] V. Caselles, R. Kimmel, Sapiro G., (1997). Geodesic active contours. International Journal of Computer Vision, 22(1), 61-79.
  • [10] Pieciak, T. (2010). Analiza odkształcen i pr˛edko´sci odkształcen mi˛esnia sercowego na podstawie obrazów rezonansu magnetycznego. Kraków: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie.
  • [11] Bołdak, C. (2008). Cyfrowe przetwarzanie obrazów. Wykłady z Cyfrowego przetwarzania obrazø’w. Retrieved from http://aragorn.pb.bialystok.pl/~boldak/DIP/CPO-W08-v01-50pr.pdf.
  • [12] Xu, C., Prince, J. L. (2014). Active Contours, Deformable Models, and Gradient Vector Flow. website, Retrieved from http://www.iacl.ece.jhu.edu/static/gvf/.
  • [13] Kochanek, D., Bartels, R. (1984). Interpolating Splines with Local Tension, Continuity, and Bias Control. Computer Graphics, 18(3), 33-41.
  • [14] Duquette, A. A., Jodoin, P. M., Bouchot, O., Lalande, A. (2012). 3D segmentation of abdominal aorta from CT-scan and MR images. Computerized Medical Imaging and Graphics, 36(4), 294-303.
  • [15] Whitaker, R. T., Xue, X., (2001). Variable- Conductance, Level-Set Curvature for Image Processing. Image Processing, 2001. Proceedings. 2001 International Conference on, 3, 142-145.
  • [16] Kitware, I. (2014). Insight Segmentation and Registration Toolkit (ITK). Website, Retrieved from http://www.itk.org.
  • [17] Kitware, I. (2014). Visualization Toolkit (VTK). Website, Retrieved from http://www.vtk.org
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-04aebfe6-2226-4e4c-a026-c75d5be7e116
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