Image analysis of self-organized multicellular patterns
Abstract Analysis of multicellular patterns is required to understand tissue organizational processes. By using a multi-scale object oriented image processing method, the spatial information of cells can be extracted automatically. Instead of manual segmentation or indirect measurements, such as general distribution of contrast or flow, the orientation and distribution of individual cells is extracted for quantitative analysis. Relevant objects are identified by feature queries and no low-level knowledge of image processing is required..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2016 |
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| Erschienen: |
2016 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2 |
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| Enthalten in: |
Current directions in biomedical engineering - 2(2016), 1 vom: 01. Sept., Seite 523-527 |
| Beteiligte Personen: |
Thies, Christian [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: |
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| Anmerkungen: |
©2016 Christian Thies et al., licensee De Gruyter. |
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| doi: |
10.1515/cdbme-2016-0116 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
GRUY002326558 |
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| 520 | |a Abstract Analysis of multicellular patterns is required to understand tissue organizational processes. By using a multi-scale object oriented image processing method, the spatial information of cells can be extracted automatically. Instead of manual segmentation or indirect measurements, such as general distribution of contrast or flow, the orientation and distribution of individual cells is extracted for quantitative analysis. Relevant objects are identified by feature queries and no low-level knowledge of image processing is required. | ||
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