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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yi-Ying Wang Lin, C.-C.K. Yung-Nien Sun |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Computer Science and Information Engineering (Yi-Ying Wang; Yung-Nien Sun) || Department of Neurology, National Cheng Kung University, Tainan, Taiwan, R.O.C (Lin, C.-C.K.) |
| Abstract | Nerve cell segmentation is important for better understanding of the connections between broken nerves. In this paper, we propose a registration-based segmentation to improve the segmentation from a series of microscopic image sequence. A global iterative closest point (ICP) registration is first employed to find the corresponding cells between neighboring frames. Then a local ICP registration is adopted to refine shape matching between corresponding cells. When registering the current frame to the previous frame, a missing cell in the current frame can obtain a dummy region with respect to the previous frame. The dummy region is then confirmed as a cell by checking the correspondence of the cell among the previous, current and next frames. Experimental results show that the proposed registration based algorithm recovers the cells that are missed in conventional segmentation. |
| Starting Page | 6726 |
| Ending Page | 6729 |
| File Size | 731044 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424432967 |
| ISSN | 1557170X |
| DOI | 10.1109/IEMBS.2009.5332851 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-03 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Microscopy Shape Iterative closest point algorithm Deformable models USA Councils Sun Mutual information Flowcharts Image sequences |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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