• Journal of Biomedical Engineering Research
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• v.38 no.3
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• pp.116-122
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• 2017

Physical breast phantoms would be useful for the development of a dedicated breast computed tomography (BCT) system and its optimization. While the conventional breast phantoms are available in compressed forms, which are appropriate for the mammography and digital tomosynthesis, however, the BCT requires phantoms in uncompressed forms. Although simple cylindrical plastic phantoms can be used for the development of the BCT system, they will not replace the roles of uncompressed phantoms describing breast anatomies for a better study of the BCT. In this study, we have designed a numerical voxel breast phantom accounting for the random nature of breast anatomies and applied it to the 3D printer to fabricate the uncompressed anthropomorphic breast phantom. The numerical voxel phantom mainly consists of the external skin and internal anatomies, including the ductal networks, the glandular tissues, the Cooper's ligaments, and the adipose tissues. The voxel phantom is then converted into a surface data in the STL file format by using the marching cube algorithm. Using the STL file, we obtain the skin and the glandular tissue from the 3D printer, and then assemble them. The uncompressed breast phantom is completed by filling the remaining space with oil, which mimics the adipose tissues. Since the breast phantom developed in this study is completely software-generated, we can create readily anthropomorphic phantoms accounting for diverse human breast anatomies.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.555-562
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• 2004

Both global volume reduction and local shape changes of hippocampus within the brain indicate their abnormal neurological states. Hippocampal shape analysis consists of two main steps. First, construct a hippocampal shape representation model ; second, compute a shape similarity from this representation. This paper proposes a novel method for the analysis of hippocampal shape using integrated Octree-based representation, containing meshes, voxels, and skeletons. First of all, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. This model is converted to intermediate binary voxel representation. And we extract the 3D skeleton from these voxels using the slice-based skeletonization method. Then, in order to acquire multiresolutional shape representation, we store hierarchically the meshes, voxels, skeletons comprised in nodes of the Octree, and we extract the sample meshes using the ray-tracing based mesh sampling technique. Finally, as a similarity measure between the shapes, we compute $L_2$ Norm and Hausdorff distance for each sam-pled mesh pair by shooting the rays fired from the extracted skeleton. As we use a mouse picking interface for analyzing a local shape inter-actively, we provide an interaction and multiresolution based analysis for the local shape changes. In this paper, our experiment shows that our approach is robust to the rotation and the scale, especially effective to discriminate the changes between local shapes of hippocampus and more-over to increase the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach.