Segmentation of Multimodal MRI of Hippocampus Using 3D Grey-Level Morphology Combined with Artificial Neural Networks

  • Roger Hult
  • Ingrid Agartz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)


This paper presents an algorithm for improving the segmentation from a semi-automatic artificial neural network (ANN) hippocampus segmentation of co-registered T1-weigthted and T2-weighted MRI data, in which the semi-automatic part is the selection of a bounding-box. Due to the morphological complexity of the hippocampus and the difficulty of separating from adjacent structures, reproducible segmentation using MR imaging is complicated.

The grey-level thresholding uses a histogram-based method to find robust thresholds. The T1-weighted data is grey-level eroded and dilated to reduce leaking from hippocampal tissue to the surrounding tissues and selecting possible foreground tissue. The method is a 3D approach, it uses 3 × 3 × 3 structure element for the grey-level morphology operations and the algorithms are applied in the three directions, sagittal, axial, and coronal, and the results are then combined together.


Segmentation Algorithm Axial Slice Kernel Density Estimate Coronal Slice Sagittal Slice 
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 2005

Authors and Affiliations

  • Roger Hult
    • 1
  • Ingrid Agartz
    • 2
  1. 1.Centre for Image AnalysisUppsala UniversityUppsalaSweden
  2. 2.Dept. Clinical Neuroscience, Human Brain InformaticsKarolinska InstitutetStockholmSweden

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