Fuzzy Spatial Growing for Glioblastoma Multiforme Segmentation on Brain Magnetic Resonance Imaging

  • Alejandro Veloz
  • Steren Chabert
  • Rodrigo Salas
  • Antonio Orellana
  • Juan Vielma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


Image segmentation is a fundamental technique in medical applications. For example, the extraction of biometrical parameter of tumors is of paramount importance both for clinical practice and for clinical studies that evaluate new brain tumor therapies.

Tumor segmentation from brain Magnetic Resonance Images (MRI) is a difficult task due to strong signal heterogeneities and weak contrast at the boundary delimitation. In this work we propose a new framework to segment the Glioblastoma Multiforme (GBM) from brain MRI. The proposed algorithm was constructed based on two well known techniques: Region Growing and Fuzzy C-Means. Furthermore, it considers the intricate nature of the GBM in MRI and incorporates a fuzzy formulation of Region Growing with an automatic initialization of the seed points.

We report the performance results of our segmentation framework on brain MRI obtained from patients of the chilean Carlos Van Buren Hospital and we compare the results with Region Growing and the classic Fuzzy C-Means approaches.


Fuzzy Spatial Growing (FSG) Magnetic Resonance Imaging (MRI) Glioblastoma Multiforme Fuzzy C-Means Region Growing Anisotropic Diffusion Filter Image Segmentation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alejandro Veloz
    • 1
  • Steren Chabert
    • 1
  • Rodrigo Salas
    • 1
  • Antonio Orellana
    • 2
  • Juan Vielma
    • 3
  1. 1.Departamento de Ingeniería Biomédica, Universidad de ValparaísoChile
  2. 2.Servicio de Neurocirugía, Hospital Carlos Van Buren, ValparaísoChile
  3. 3.Servicio de Imagenología Compleja, Hospital Carlos Van Buren, ValparaísoChile

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