Segmentation strategy for enhanced MR cystography based on graph theory
DUAN Chaijie1,2, ZHANG Yijia1,2, GUO Hui1,2, YE Datian1,2, LIANG Zhengrong3
1. Shenzhen Key Laboratory for Nondestructive and Minimal Invasive Medical Technology, Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;
2. Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China;
3. Stony Brook University, Stony Brook NY 11794, USA
Abstract：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.
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