Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
ACM SIGGRAPH 2003 Papers
Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Stitching Using Structure Deformation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Optimal multiple-seams search for image resizing with smoothness and shape prior
The Visual Computer: International Journal of Computer Graphics
Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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Four dimensional CT (4D CT) provides a way to reduce positional uncertainties caused by respiratory motion. Due to the inconsistencies of patient's breathing, images from different respiratory periods may be misaligned, thus the acquired 3D data may not accurately represent the anatomy. In this paper, we propose a method based on graph algorithms to reduce the magnitude of artifacts present in helical 4D CT images. The method strives to reduce the magnitude of artifacts directly from the reconstructed images. The experiments on simulated data showed that the proposed method reduced the landmarks distance errors from 2.7 mm to 1.5 mm, outperforming the registration methods by about 42%. For clinical 4D CT image data, the image quality was evaluated by the three medical experts and both of who identified much fewer artifacts from the resulting images by our method than from those by the commercial 4D CT software.