An adaptive gradient-based boundary detector for MRI images of the brain

Some methods for segmenting MRI data, especially those which employ multi-spectral data, can be very time-consuming. Hence, it is desirable to reduce the dimensionality of the multi-spectral data by eliminating regions which are of no interest. When, for example, the objective is the segmentation of the brain, it is clear that a considerable amount of the image could be discarded without compromising (in some cases even enhancing) the quality of the segmented image. This paper presents an algorithm to isolate the brain from the extra-cranial tissues irrespective of the nature of the MRI brain data (sagittal, coronal or axial slices). Given a starting point, or seed, at the boundary (or near it) between the intra-cranial and extra-cranial region, the algorithm “walks” along the boundary by calculating at each point the direction of the local edge. The gradient operator chosen to calculate the edge direction was the integrated directional derivative gradient (IDDG) operator as defined by Zuniga and Haralick (1987) and is taken from the row and column directional derivatives, D_r and D_c