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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hang Chang Auer, M. Parvin, B. |
| Copyright Year | 2009 |
| Description | Author affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory, CA 94720, USA (Hang Chang; Auer, M.; Parvin, B.) |
| Abstract | Biological images have the potential to reveal complex signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition of a specimen based on user-defined patterns of texture and contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here, an interactive computational model is introduced for learning patterns of interest by example. The learned patterns bound an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. First, the energy function is defined according to the curve evolution. Next, a graph is constructed with weighted edges on the energy function and is optimized with the graph cut algorithm. As a result, the method combines the advantages of the level set method and graph cut algorithm, i.e., “topological” invariance and computational efficiency. The technique is extended to the multiphase segmentation problem; the method is validated on synthetic images and then applied to specimens imaged by transmission electron microscopy(TEM). |
| Starting Page | 1103 |
| Ending Page | 1106 |
| File Size | 571241 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424439317 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2009.5193249 |
| 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 | Robust stability Optimization methods Biological system modeling Shape Image texture analysis Pattern analysis Computational modeling Active contours Evolution (biology) Level set Electron Microscopy Interactive learning Active Contour Graph Cut Texture Segmentation |
| Content Type | Text |
| Resource Type | Article |
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