Segmentation strategy for enhanced MR cystography based on graph theory

Fast magnetic resonance (MR) bladder scans with artifacts and low signal to noise ratios (SNR) are used to precisely segment and achieve the bladder wall. The short scans are registered to a selected reference using an affine transformation followed by a hierarchical B-spline registration. The average of the registration results is the motion-corrected image. The graph cut method based on a closed-set model is then used segment the bladder MR image. The strategy is evaluated using both computer-generated images and clinical MR images. The results show that the average motion-corrected image with a high SNR (i.e., 3.26 for the simulated images and 2.17 for the clinical images) and less artifacts followed by a graph-cut segmentation tends to have a more accurate result. This strategy reduces the artifacts and improves the SNR to provide high resolution segmentation of the bladder wall.