Correction to: Multi-template matching: a versatile tool for object-localization in microscopy images
The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit whe...
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| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
Jan 4 2022
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| In: |
BMC bioinformatics
Year: 2022, Volume: 23, Pages: 1 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-021-04524-7 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12859-021-04524-7
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| Author Notes: | Laurent S.V. Thomas and Jochen Gehrig |
MARC
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| 520 | |a The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit when samples are poorly contrasted with the background, or when only parts of larger structures should be detected. Furthermore, the development of such pipelines requires substantial engineering of analysis workflows and often results in case-specific solutions. Therefore, we propose a new straightforward and generic approach for object-localization by template matching that utilizes multiple template images to improve the detection capacity. | ||
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