Automatic detection of brain contours in MRI data sets

  • M E Brummer
  • R M Mersereau
  • R L Eisner
  • R R J Lewine
4. Segmentation: Specific Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 511)


An algorithm is presented for fully automated detection of brain contours from single-echo 3-D coronal MRI data. The technique detects structures in a head data volume in a hierarchical fashion. Detections consist of histogram-based thresholding operation, followed by a morphological cleanup procedure of the binary threshold mask images. Anatomic knowledge, essential for the discrimination between desired and undesired structures, is implemented through a sequence of conventional and new morphological operations. Innovative use of 3-D distance transformations allows implicit evaluation of anatomic relationships for structure recognition. Overlap tests between neighbouring slice images are used to propagate coherent 2-D brain masks through the third dimension. A summary of results of testing the algorithm on 23 test data sets is presented, with a discussion of potential for clinical application and generalization to other problems, and of limitations of the technique.


Image segmentation contour detection MRI brain imaging 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • M E Brummer
    • 1
  • R M Mersereau
    • 2
  • R L Eisner
    • 3
  • R R J Lewine
    • 4
  1. 1.Department of RadiologyEmory University School of MedicineAtlantaUSA
  2. 2.School of Electrical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Carlyle Fraser Heart CenterEmory University Hospital at Crawford LongAtlantaUSA
  4. 4.Department of PsychiatryEmory University School of MedicineAtlantaUSA

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