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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wei Liao Rohr, K. Chang-Ki Kang Zang-Hee Cho Worz, S. |
| Copyright Year | 2012 |
| Description | Author affiliation: Neuroscience Research Institute, Gachon University of Medicine and Science, Namdonggu, Incheon, 405-760, Republic of Korea (Chang-Ki Kang; Zang-Hee Cho) || University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany (Wei Liao; Rohr, K.; Worz, S.) |
| Abstract | Accurate 3D models of the human brain vessels can greatly help to diagnose serious diseases. Such models can be constructed by segmentation of 3D MRA images, especially the recently introduced high resolution 7T MRA. We propose a new two-step approach for fully automatic segmentation of 7T MRA images of the human cerebrovascular system. First, a 3D model-based approach is applied to segment thick vessels and most parts of thin vessels. Then, the missing vessel parts, which are caused by low contrast and high noise, are completed using a novel fast marching approach with anisotropic directional prior. An evaluation of our approach and a comparison with two previous approaches have been conducted using high resolution 3D 7T MRA images. |
| Starting Page | 1140 |
| Ending Page | 1143 |
| File Size | 719699 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457718571 |
| ISSN | 19457928 |
| e-ISBN | 9781457718588 |
| DOI | 10.1109/ISBI.2012.6235761 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-02 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Humans Biomedical imaging Solid modeling Brain modeling Mathematical model Computational modeling anisotropic fast marching Automatic 3D segmentation Cerebral vasculature 7T MRA |
| Content Type | Text |
| Resource Type | Article |
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