Segmentation of T1-MRI of the Human Cortex Using a 3D Grey-Level Morphology Approach

  • Roger Hult
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

In this paper, an algorithm for fully automatic segmentation of the cortex from T1-weighted transversal, coronal, or sagittal MRI data is presented. The segmentation algorithm uses a histogram-based method to find accurate threshold values. There are four initial masks created: first two thresholded masks from the original volume, providing background and brain tissue; then a third mask thresholded from a 3D grey-level eroded version of the volume, providing brain tissue; and lastly a fourth mask thresholded from a 3D grey-level dilated version of the volume, providing surrounding fat. On the start slice of these masks binary morphological operations and logical operations are used; then the rest of the slices are segmented using information from the previous slice combined with the other masks. Information from earlier slices is propagated to keep the segmented volume from leaking into non-brain tissue.

Keywords

Kernel Density Estimate Magnetic Resonance Imaging Data Brain Matter Human Cortex Outermost Part 
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 2003

Authors and Affiliations

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

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