title: Automatic detection of brain contours in MRI data sets.
abstract: 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.
authors.affiliation: Dept. of Radiol., Emory Univ. Sch. of Med., Atlanta, GA.
authors.forename: M E
authors.initials: ME
authors.lastname: Brummer
authors.forename: R M
authors.initials: RM
authors.lastname: Mersereau
authors.forename: R L
authors.initials: RL
authors.lastname: Eisner
authors.forename: R J
authors.initials: RJ
authors.lastname: Lewine