SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy
Although there are many reconstruction algorithms for localization microscopy, their use is hampered by the difficulty to adjust a possibly large number of parameters correctly. We propose SimpleSTORM, an algorithm that determines appropriate parameter settings directly from the data in an initial s...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
11 April 2014
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| In: |
Histochemistry and cell biology
Year: 2014, Jahrgang: 141, Heft: 6, Pages: 613-627 |
| ISSN: | 1432-119X |
| DOI: | 10.1007/s00418-014-1211-4 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00418-014-1211-4
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| Verfasserangaben: | Ullrich Köthe, Frank Herrmannsdörfer, Ilia Kats, Fred A. Hamprecht |
MARC
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| 520 | |a Although there are many reconstruction algorithms for localization microscopy, their use is hampered by the difficulty to adjust a possibly large number of parameters correctly. We propose SimpleSTORM, an algorithm that determines appropriate parameter settings directly from the data in an initial self-calibration phase. The algorithm is based on a carefully designed yet simple model of the image acquisition process which allows us to standardize each image such that the background has zero mean and unit variance. This standardization makes it possible to detect spots by a true statistical test (instead of hand-tuned thresholds) and to de-noise the images with an efficient matched filter. By reducing the strength of the matched filter, SimpleSTORM also performs reasonably on data with high-spot density, trading off localization accuracy for improved detection performance. Extensive validation experiments on the ISBI Localization Challenge Dataset, as well as real image reconstructions, demonstrate the good performance of our algorithm. | ||
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