Semi-automatic 3-D segmentation of Computed Tomographic imagery by iterative gradient-driven volume growing

We propose a novel gradient driven methodology for three dimensional (3-D) segmentation of Computed Tomographic (CT) imagery. Our approach begins interactively where-in a user marks a set of voxels within the cross-section of a Sub-Volume Of Interest (SVOI), using a single slice of the CT volume. Subsequently, a 3-D gradient detection scheme is utilized to determine the radiodensity variations across the volume. The resultant gradient information is employed in an iterative volume growing procedure, which is initiated at voxels with small gradient magnitudes adjoining the user-selected voxels and culminates at voxels with large gradient magnitudes, to arrive at the final 3-D segmentation result of the SVOI. The aforementioned method was tested on multiple studies and the results show favorable performance against a state-of-the-art technique.