Automatic detection of brain contours in MRI data sets

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.