Cellular Automata Segmentation of Brain Tumors on Post Contrast MR Images

  • Andac Hamamci
  • Gozde Unal
  • Nadir Kucuk
  • Kayihan Engin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)


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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