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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Peng Shi Xiaobo Zhou Qing Li Baron, M. Teylan, M.A. Yong Kim Wong, S.T.C. |
| Copyright Year | 2009 |
| Description | Author affiliation: Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA (Peng Shi; Xiaobo Zhou; Qing Li; Wong, S.T.C.) || Laboratory of Molecular and Cellular Neuroscience, The Rockefeller University, New York, 10065, USA (Baron, M.; Teylan, M.A.; Yong Kim) |
| Abstract | Recent studies on neuron imaging show that there is a strong relationship between the functional properties of a neuron and its morphology, especially its dendritic spine structures. However, most of the current methods for morphological spine classification only concern features in two-dimensional (2D) space, which consequently decreases the accuracy of dendritic spine analysis. In this paper, we propose a semi-supervised learning (SSL) framework, in which spine phenotypes in three-dimensional (3D) space are considered. With training only on a few pre-classified inputs, the rest of the spines can be identified effectively. We also derived a new scheme using an affinity matrix between features to further improve the accuracy. Our experimental results indicate that a small training dataset is sufficient to classify detected dendritic spines. |
| Starting Page | 1019 |
| Ending Page | 1022 |
| File Size | 222760 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424439317 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2009.5193228 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Semisupervised learning Neurons Shape Image segmentation Microscopy Surface morphology Hospitals Head Neck Biotechnology morphological spine classification dendritic spine semi-supervised learning |
| Content Type | Text |
| Resource Type | Article |
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