Image analysis of self-organized multicellular patterns

Christian Thies; Galina Khachaturyan; Assaf Zemel; Ralf Kemkemer

doi:10.1515/cdbme-2016-0116

Image analysis of self-organized multicellular patterns

Christian Thies^*
, Galina Khachaturyan
, Assaf Zemel
, Ralf Kemkemer

^*Corresponding author for this work

Institute of Biomedical Research at the Faculty of Dental Medicine

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English
Pages (from-to)	523-527
Number of pages	5
Journal	Current Directions in Biomedical Engineering
Volume	2
Issue number	1
DOIs	https://doi.org/10.1515/cdbme-2016-0116
State	Published - Sep 2016

Bibliographical note

Publisher Copyright:
© 2016 Christian Thies et al., licensee De Gruyter.

Keywords

Cell segmentation
Image analysis
Object extraction
Phase contrast micrographs
Spatial information
Tissue organization

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10.1515/cdbme-2016-0116

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KW - Cell segmentation

KW - Image analysis

KW - Object extraction

KW - Phase contrast micrographs

KW - Spatial information

KW - Tissue organization

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Image analysis of self-organized multicellular patterns

Abstract

Bibliographical note

Keywords

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