Cellular Automata Segmentation of Brain Tumors on Post Contrast MR Images

  • Andac Hamamci
  • Gozde Unal
  • Nadir Kucuk
  • Kayihan Engin
Conference paper

DOI: 10.1007/978-3-642-15711-0_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)
Cite this paper as:
Hamamci A., Unal G., Kucuk N., Engin K. (2010) Cellular Automata Segmentation of Brain Tumors on Post Contrast MR Images. In: Jiang T., Navab N., Pluim J.P.W., Viergever M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6363. Springer, Berlin, Heidelberg

Abstract

In this paper, we re-examine the cellular automata(CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andac Hamamci
    • 1
  • Gozde Unal
    • 1
  • Nadir Kucuk
    • 2
  • Kayihan Engin
    • 2
  1. 1.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey
  2. 2.Department of Radiation OncologyAnadolu Medical CenterKocaeliTurkey

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