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2014 | 2 | 219--225
Tytuł artykułu

Topological Prostate Segmentation Method in MRI

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main aim of this paper is to advance the state of the art in automated prostate segmentation using T2 weighted MR images, by introducing a hybrid topological MRI prostate segmentation method which is based on a set of pre-labeled MR atlas images. The proposed method has been experimentally tested on a set of 30 MRI T2 weighted images. For evaluation, segmentations obtained by applying the proposed method have been compared with the manual segmentations, using an average Dice Similarity Coefficient (DSC). Obtained quantitative results have shown a good approximation of the segmented prostate.(original abstract)
Rocznik
Tom
2
Strony
219--225
Opis fizyczny
Twórcy
  • Goce Delcev University in Stip, Macedonia
autor
  • Goce Delcev University in Stip, Macedonia
Bibliografia
  • Álvarez, C., Martínez, F., Romero, E., "A novel atlas-based approach for MRI prostate segmentation using multiscale points of interest ", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 89220O (November 19, 2013), http://dx.doi.org/10.1117/12.2035462
  • Catalona, W. J., Smith, D. S., Ratliff T. L., Dodds, K. M., Coplen, D. E., Yuan, J. J., Petros, J.A., Andriole, G. L., "Measurement of prostate-specific antigen in serum as a screening test for prostate cancer", New England Journal of Medicine, vol. 324, no. 17, pp. 1156-1161, 1991, http://dx.doi.org/10.1056/NEJM199104253241702
  • Cootes, T. F., Hill, A., Taylor, C. J., Haslam, J., The Use of Active Shape Model for Locating Structures in Medical Images, Image and Vision Computing 12 (1994)355-66, http://dx.doi.org/10.1007/BFb0013779
  • Ghose, S., Oliver, A., Marti, R., Llado, X., Freixenet, J., Vilanova, J. C., Meriaudeau, F. A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance. In Image Processing (ICIP), 2011 18th IEEE International Conference on (pp. 713-716). 2011, September IEEE, http://dx.doi.org/10.1109/ICIP.2011.6116653
  • Ghose, S., Oliver, A., Martí, R., Lladó, X., Vilanova, J. C., Freixenet, J., Mitra, J., Sidibé, D., Meriaudeau, F. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Computer methods and programs in biomedicine, 108(1), 262-287, 2012, http://dx.doi.org/10.1016/j.cmpb.2012.04.006
  • GLOBOCAN 2012 (online at http://globocan.iarc.fr, last visited 19.04.2014).
  • Halpern, E. J., Cochlin, D. L., Goldberg, B. B., Imaging of the prostate, Informa Healthcare, United Kingdom, first edition, 2002
  • Kim, S. G., Seo, Y. G., A TRUS Prostate Segmentation using Gabor Texture Features and Snake-like Contour. Journal of Information Processing Systems, 9(1), 2013, http://dx.doi.org/10.3745/JIPS.2013.9.1.103
  • Klein, S., Van der Heide, U. A., Lipps, I. M., Vulpen, M. V., Staring, M., Pluim, J. P. W.,"Automatic Segmentation of the Prostate in 3D MR Images by Atlas Matching Using Localized Mutual Information", Medical Physics 35 (2008) 1407-17, http://dx.doi.org/10.1118/1.2842076
  • Langerak, T. R., Berendsen, F. F., Van der Heide, U. A., Kotte, A. N., Pluim, J. P. Multiatlas-based segmentation with preregistration atlas selection. Medical physics, 40(9), 2013, 091701, http://dx.doi.org/10.1118/1.4816654
  • Makni, N., Iancu, A., Colot, O., Puech, P., Mordon, S., Betrouni, N., et al.: Zonal segmentation of prostate using multispectral magnetic resonance images. Medical Physics 38(11), 6093 (2011), http://dx.doi.org/10.1118/1.3651610
  • Natarajan, S., Marks, L. S., Margolis, D. J., Huang, J., Macairan, M. L., Lieu, P., & Fenster, A. Clinical application of a 3D ultrasoundguided prostate biopsy system. In Urologic Oncology: Seminars and Original Investigations, Vol. 29, No. 3, pp. 334-342, June 2011, Elsevier, http://dx.doi.org/10.1016/j.urolonc.2011.02.014
  • Ozer, S., Langer, D. L., Liu, X., Haider, M. A., Van der Kwast, T. H., Evans, A. J., Yang, Y., Wernick, M. N., Yetik,I. S., Supervised and Unsupervised Methods for Prostate Cancer Segmentation with Multispectral MRI, Medical Physics 37 (2010) 1873-83, http://dx.doi.org/10.1118/1.3359459
  • Prostate MR Image Database, The Brigham and Women's Hospital, 2008 (Online at: http://prostatemrimagedatabase.com; last accessed 19.04.2014).
  • Puech, P., Betrouni, N., Makni, N., Dewalle, A.S., Villers, A., Lemaitre, L., Computer-Assisted Diagnosis of Prostate Cancer Using DCE-MRI Data: Design, Implementation and Preliminary Results, International Journal of Computer Assisted Radiology and Surgery 4 (2009) 1-10, http://dx.doi.org/10.1007/s11548-008-0261-2
  • Samiee, M., Thomas, G., Fazel-Rezai, R., Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation, in: IEEE International Symposium on Signal Processing and Information Technology, IEEE Computer Society Press, USA, 2006, pp. 203-7, http://dx.doi.org/10.1109/ISSPIT.2006.270797
  • Sjöberg, C., & Ahnesjö, A. Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures. Computer methods and programs in biomedicine, 110(3), 2013, pp.308-319, http://dx.doi.org/10.1016/j.cmpb.2012.12.006
  • Vikal, S., Haker, S., Tempany, C., Fichtinger, G., Prostate Contouring in MRI Guided Biopsy, in: J. P. W. Pluim, B. M. Dawant (Eds.), Proceedings of SPIE Medical Imaging: Image Processing, SPIE, USA, 2009, pp. 7259-72594A, http://dx.doi.org/ 10.1117/12.812433.
  • Woodruff, A.J., Morgan, T.M., Wright, J.L., Porter, C.R.., Prostate volume as an independent predictor of prostate cancer and high-grade disease on prostate needle biopsy. J Clin Oncol 26: 5165, 2008
  • Xie, Q., Ruan, D. Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics. Medical physics, 41(4), 2014, 041909, http://dx.doi.org/10.1118/1.4867855
  • Zhu, Y., Williams, S., Zwiggelaar, R., A Hybrid ASM Approach for Sparse Volumetric Data Segmentation, Pattern Recognition and Image Analysis 17 (2007) 252-8, http://dx.doi.org/10.1134/S1054661807020125
  • Zwiggelaar, R., Zhu, Y., Williams, S., Semi-Automatic Segmentation of the Prostate, in: F. J. Perales, A. J. Campilho, N. P. de la Blanca, A. Sanfeliu (Eds.), Pattern Recognition and Image Analysis, Proceedings of First Iberian Conference, IbPRIA, Springer, Berlin and Heidelberg and New York and Hong Kong and London and Milan and Paris and Tokyo, 2003, pp. 1108-16, http://dx.doi.org/10.1007/978-3-540-44871-6_128
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171325109

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