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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fuhui Long Hanchuan Peng Myers, E. |
| Copyright Year | 2007 |
| Description | Author affiliation: Janelia Farm Res., Howard Houghes Med. Inst., Ashburn, VA (Fuhui Long; Hanchuan Peng; Myers, E.) |
| Abstract | Automatic segmentation of nuclei in 3D microscopy images is essential for many biological studies including high throughput analysis of gene expression level, morphology, and phenotypes in single cell level. The complexity and variability of the microscopy images present many difficulties to the traditional image segmentation methods. In this paper, we present a new method based on 3D watershed algorithm to segment such images. By using both the intensity information of the image and the geometry information of the appropriately detected foreground mask, our method is robust to intensity fluctuation within nuclei and at the same time sensitive to the intensity and geometrical cues between nuclei. Besides, the method can automatically correct potential segmentation errors by using several post-processing steps. We tested this algorithm on the 3D confocal images of C.elegans, an organism that has been widely used in biological studies. Our results show that the algorithm can segment nuclei in high accuracy despite the non-uniform background, tightly clustered nuclei with different sizes and shapes, fluctuated intensities, and hollow-shaped staining patterns in the images |
| Starting Page | 536 |
| Ending Page | 539 |
| File Size | 535354 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424406714 |
| DOI | 10.1109/ISBI.2007.356907 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Microscopy Clustering algorithms Throughput Image analysis Gene expression Morphology Cells (biology) Information geometry Robustness |
| Content Type | Text |
| Resource Type | Article |
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