Automated Cell Segmentation in Phase-Contrast Images based on Classification and Region Growing
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Proceedings of 2015 IEEE International Symposium on Biomedical Imaging, 2015. |
| MU Faculty or unit | |
| Citation | |
| web | https://ieeexplore.ieee.org/document/7164149 |
| Doi | http://dx.doi.org/10.1109/ISBI.2015.7164149 |
| Field | Use of computers, robotics and its application |
| Keywords | phase-contrast microscopy; segmentation; classification; superpixel; cells |
| Description | Cell segmentation in phase-contrast microscopy images remains a challenging problem because of the large variability in subcellular structures and imaging artifacts. In this paper, we present an approach to the automatic segmentation of tightly packed cells in phase-contrast images. We combine the classification of superpixels with the region-growing method to locate cell membrane boundaries. We demonstrate that such a combined approach is able to perform the task of cell detection and segmentation with a high level of precision. On the presented dataset, we achieved 90% precision with 78% recall. The results indicate that this method is suitable for real biological applications. |
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