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Fast 3D Segmentation of Hepatic Images Combining Region and Boundary Criteria

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A new approach to the liver segmentation from 3D images is presented and compared to the existing methods in terms of quality and speed of segmentation. The proposed technique is based on 3D deformable model (active surface) combining boundary and region information. The segmentation quality is comparable to the existing methods but the proposed technique is significantly faster. The experimental evaluation was performed on clinical datasets (both MRI and CT), representing typical as well as more challenging to segment liver shapes.
Twórcy
autor
  • Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland
autor
  • Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland
autor
  • Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland
Bibliografia
  • [1] M.A. Ballester, A.P. Zisserman, M. Brady, Estimation of the partial volume effect in MRI, Medical Image Analysis, 6(4):389 - 405, 2002
  • [2] A. Bornik, R. Beichel, and D. Schmalstieg. Interactive editing of segmented volumetric datasets in a hybrid 2-D/3-D virtual environment. In Proc. of ACM Symp. Virtual Reality Software Technol., 197-206, 2009
  • [3] P. Campadelli, E. Casiraghi, and A. Esposito. Liver segmentation from computed tomography scans: A survey and a new algorithm. Artificial Intelligence in Medicine, 45:185-196, 2009 [PubMed]
  • [4] L.D. Cohen. On active contour models and balloons. CVGIP: Image Underst., 53:211-218, 1991 [CrossRef]
  • [5] Ch. Florin, N. Paragios, F. Gareth and J. Williams. Liver segmentation using sparse 3D prior models with optimal data support. In Proc. of the 20th Int. Conf. on Information Processing in Medical Imaging, 38-49, 2007
  • [6] l. Gao, D. Heath and E. Fishman. Abdominal image segmentation using three-dimensional deformable models. Investigative Radiology, 33(6):348-55, 1998 [PubMed] [CrossRef]
  • [7] T. Heimann, B. van Ginneken, M.A. Styner, et al. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. on Medical Imaging, 28:1251-1265, 2009 [Web of Science]
  • [8] X. Huang, D. Metaxas and T. Chen. Metamorphs: deformable shape and appearance models. IEEE Trans. Pattern Anal. Mach. Intell., 30(8):1444-1459, 2008 [Web of Science]
  • [9] M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. Int. Journal of Computer Vision, 1(4):321-331, 1988
  • [10] T.McInerney and D. Terzopoulos. T-snakes: Topology adaptive snakes. Medical Image Analysis, 4(2), 2000 [CrossRef]
  • [11] T. McInerney and D. Terzopoulos. Topology adaptive deformable surfaces for medical image volume segmentation. IEEE Trans. on Image Processing, 18(10):840-850, 1999
  • [12] H.P. Meinzer, M. Thorn, and C.E. Cardenas. Computerized planning of liver surgery - an overview. Computer and Graphics, 26(4):569-576, 2002
  • [13] J.V. Miller, D.E. Breen, W.E. Lorensen, et al. Geometrically deformed models: a method for extracting closed geometric models form volume data. SIGGRAPH Comput. Graph., 25(4):217-226, 1991 [CrossRef]
  • [14] D. Reska and M. Kretowski. HIST - an application for segmentation of hepatic images. Zeszyty Naukowe Politechniki Bialostockiej. Informatyka, 7:71-93, 2011
  • [15] A. Schenk, G. Prause, and HO. Peitgen.X. Efficient Semiautomatic Segmentation of 3D Objects in Medical Images. In Proc. of Medical Image Computing and Computer-assisted Intervention, 186-195, 2000
  • [16] K. Seo, H. Kim, T. Park, P. Kim, and J. Park. Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing. In Proc. of the First Int. Conf. on Advances in Natural Computation - Volume Part I, 1027-1030, 2005
  • [17] S. Thon, G. Gesquiere and R. Raffin. A low Cost Antialiased Space Filled Voxelization Of Polygonal Objects. GraphiCon 2004, pp. 71-78, 2004
  • [18] C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images. In In Proc. of the Sixth Int. Conf. on Computer Vision, IEEE Computer Society, 1998
  • [19] G. Tsechpenakis. Deformable Model-based Medical Image Segmentation. Multi Modality State-ofthe- Art Medical Image Segmentation and Registration Methodologies, Springer Publishing, 2011
  • [20] C. Xu and J. Prince. Snakes, shapes, and gradient vector flow. IEEE Trans. on Image Processing, 7(3):359-369, 1998.
  • [21] W.N.J.W. Yussof and H. Burkhardt. 3D Volumetric CT Liver Segmentation Using Hybrid Segmentation Techniques. In Proc. of the 2009 Int. Conf. of Soft Computing and Pattern Recognition, 7(3):404-408, 2009
  • [22] X. Zhang, J. Tian, K. Deng, Y. Wu, X. Li, Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection. IEEE Trans. on Biomedical Engineering, 57(10):2622-2626, 2010 [Web of Science]
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8f4cac68-fe63-4cda-8eca-ad9901ea012c
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