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MRI Brain Segmentation Using Cellular Automaton Approach

  • Rafał Henryk Kartaszyński
  • Paweł Mikołajczak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)

Abstract

In this article new approach to the MRI brain segmentation is presented. It is based on the Cellular Automaton (CA). The method is truly interactive, and produces very good results comparable to those achieved by Random Walker or Graph Cut algorithms. It can be used for CT and MRI images, and is accurate for various types of tissues. Discussed here version of the algorithm is developed especially for the purpose of the MRI brain segmentation. This method is still in the phase of development and therefore can be improved, thus final version of the algorithm can differ from the one presented here. The method is extensible, allowing simple modification of the algorithm for a specific task. As we will also see it is very accurate for two-dimensional medical images. Three-dimensional cases require some unsophisticated data post processing [1], or making some modifications in the manner in which the automaton grows into the third dimension from the two-dimensional layer. To prove quality of this method, some tests results are presented at the end of this paper.

Keywords

Cellular Automaton Seed Point Neighboring System Connected Component Label Label Bacterium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rafał Henryk Kartaszyński
    • 1
  • Paweł Mikołajczak
    • 1
  1. 1.Department of Computer ScienceMaria Curie Skłodowska UniversityLublinPoland

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