Modified Expectation Maximization Algorithm for MRI Segmentation

  • Ramiro Donoso
  • Alejandro Veloz
  • Héctor Allende
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)


Magnetic Resonance Image segmentation is a fundamental task in a wide variety of computed-based medical applications that support therapy, diagnostic and medical applications. In this work, spatial information is included for estimating paramaters of a finite mixture model, with gaussian distribution assumption, using a modified version of the well-know Expectation Maximization algorithm proposed in [3]. Our approach is based on aggregating a transition step between E-step and M-step, that includes the information of spatial dependences between neighboring pixels.

Our proposal is compared with other approaches proposed in the image segmentation literature using the size and shape test, obtaining accurate and robust results in the presence of noise.


Expectation Maximization algorithm Finite Mixture models spatial information Magnetic Resonance Imaging segmentation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ramiro Donoso
    • 1
  • Alejandro Veloz
    • 1
  • Héctor Allende
    • 1
  1. 1.Departamento de InformáticaUniversidad Técnica Federico Santa MaríaValparaísoChile

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