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Automatic detection of brain contours in MRI data sets

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4 Author(s)
Brummer, M.E. ; Dept. of Radiol., Emory Univ. Sch. of Med., Atlanta, GA, USA ; Mersereau, R.M. ; Eisner, R.L. ; Lewine, R.R.J.

A software procedure is presented for fully automated detection of brain contours from single-echo 3-D MRI data, developed initially for scans with coronal orientation. The procedure detects structures in a head data volume in a hierarchical fashion. Automatic detection starts with a histogram-based thresholding step, whenever necessary preceded by an image intensity correction procedure. This step is followed by a morphological procedure which refines the binary threshold mask images. Anatomical knowledge, essential for the discrimination between desired and undesired structures, is implemented in this step through a sequence of conventional and novel morphological operations, using 2-D and 3-D operations. A final step of the procedure performs overlap tests on candidate brain regions of interest in neighboring slice images to propagate coherent 2-D brain masks through the third dimension. Results are presented for test runs of the procedure on 23 coronal whole-brain data sets, and one sagittal whole-brain data set. Finally, the potential of the technique for generalization to other problems is discussed, as well as limitations of the technique

Published in:

Medical Imaging, IEEE Transactions on  (Volume:12 ,  Issue: 2 )