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Survey of Modern Image Segmentation Algorithms on CT Scans of Hydrocephalic Brains

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Języki publikacji
EN
Abstrakty
EN
Paper presents the concept of applying image segmentation algorithms for precise extraction of cerebrospinal fluid (CSF) from CT brain scans. Accurate segmentation of the CSF from the intracranial brain area is crucial for further reliable analysis and quantitative assessment of hydrocephalus. Presented research was aimed at the comparison of effectiveness of three modern segmentation approaches used for this purpose. Specifically, random walk, level set and min-cut/max-flow algorithms were considered. The visual and numerical comparison of the segmentation results leads to conclusion that the most effective algorithm for the considered problem is level set, although the positive medical verification of the results revealed that either of considered algorithms can be successfully applied in the diagnostic applications.
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Twórcy
  • Institute of Applied Computer Science, Lodz University of Technology, Lodz, Poland
  • Institute of Applied Computer Science, Lodz University of Technology, Lodz, Poland
Bibliografia
  • [1] S. Kobashi, T. Takae, Y. Hata, Y.T. Kitamura, T. Yanagida, O. Ishikawa, M. Ishikawa, Automated segmentation of the cerebrospinal fluid and the lateral ventricles from human brain MR images, Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, Vol. 4, pp. 1961-1966, 2001
  • [2] J. Liu, S. Huang, W.L. Nowinski, Automatic segmentation of the human brain ventricles from MR images by knowledge-based region growing and trimming, Neuroinformatics, Vol. 7, pp. 131-146, 2009
  • [3] V. Gupta, W. Ambrosius, G. Qian, A. Blazejewska, R. Kazmierski, A. Urbanik, W.L. Nowinski, Automatic Segmentation of Cerebrospinal Fluid, White and Gray Matter in Unenhanced Computed Tomography Images, Academic Radiology, Vol. 17, No. 11, pp. 1350-1358, 2010
  • [4] U.E. Ruttimann, E.M. Joyce, D.E. Rio, M.J. Eckardt, Fully automated segmentation of cerebrospinal fluid in computed tomography, Psychiatry Research: Neuroimaging, Vol. 50, No. 2, pp. 101-119, 1993
  • [5] H.G. Schnack, H.E. Hulshoff Pol, W.F.C. Baarè, M.A. Viergever, R.S. Kahn, Automatic segmentation of the ventricular system from MR images of the human brain, NeuroImage, Vol. 14, pp. 95-104, 2001
  • [6] T.H. Lee, M.F.A. Fauzi, R. Komiya, Segmentation of CT brain images using K-means and EM cluster ing, In Computer Graphics, Imaging and Visualisation, CGIV’08. Fifth International Conference on IEEE, pp. 339-344, 2008
  • [7] W. Halberstadt, T.S. Douglas, Fuzzy clustering of CT images for the measurement of hydrocephalus associated with tuberculous meningitis, In Engineering in Medicine and Biology Society, IEEEEMBS 2005, 27th Annual International Conference of the IEEE, pp. 4014-4016, 2006
  • [8] F. Luo, J.W. Evans, N.C. Linney, M.H. Schmidt, P.H. Gregson, Wavelet-based image registration and segmentation framework for the quantitative evaluation of hydrocephalus, Journal of Biomedical Imaging, Vol. 2010, 2010
  • [9] J.A. Butman, M.G. Linguraru, Assessment of ventricle volume from serial MRI scans in communicating hydrocephalus, 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 49-52, 2008
  • [10] X. Zang, Y. Wang, J. Yang, Y. Liu, A novel method of CT brain images segmentation, International Conference on Medical Image Analysis and Clinical Application, pp. 109-112, 2010
  • [11] L. Grady, Random Walks for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, pp. 1768-1783, 2006
  • [12] S. Osher, J.A. Sethian, Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi Formulations, Journal of Computational Physics, Vol. 79, pp. 12-49, 1988
  • [13] S. Osher, N. Paragios, Geometric Level Set Methods in Imaging Vision and Graphics, Springer Verlag, 2003
  • [14] Y. Boykov, M.P. Jolly, Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images, Proceedings of Internation Conference on Computer Vision, Vol. 1, pp. 105-112, 2001
  • [15] Y. Boykov, V. Kolmogorov, An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision, IEEE Transactions on PAMI, Vol. 26, No. 9, pp. 1124-1137, 2004
  • [16] J. Makhoul, F. Kubala, R. Schwartz, R. Weischedel, Performance measures for information extraction, Proceedings of DARPA Broadcast News Workshop, Herndon, 1999
  • [17] D.L. Olson, D. Delen, Advanced Data Mining Techniques, Springer, 1st edition, 2008
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b719683e-1d58-40cf-ba33-a7b7d3a06af6
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