Image analysis of Arabidopsis trichome patterning in 4D confocal datasets

In this article, we present an approach for the automated extraction of quantitative information about trichome patterning on leaves of Arabidopsis thaliana. Time series of growing rosette leaves (4D confocal datasets, 3D + time) are used for this work. At first, significant anatomical structures, i...

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Bibliographic Details
Published in2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro pp. 742 - 745
Main Authors Bensch, R., Ronneberger, O., Greese, B., Fleck, C., Wester, K., Hulskamp, M., Burkhardt, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Anatomical structure
Arabidopsis trichome patterning
Biology
Computed tomography
Data mining
Image analysis
Optimized production technology
registration
Robustness
Support vector machines
surface extraction
Surface morphology
symmetry plane
trichome localization
X-ray imaging
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Summary:In this article, we present an approach for the automated extraction of quantitative information about trichome patterning on leaves of Arabidopsis thaliana. Time series of growing rosette leaves (4D confocal datasets, 3D + time) are used for this work. At first, significant anatomical structures, i.e. leaf surface and midplane are extracted robustly. Using the extracted anatomical structures, a biological reference coordinate system is registered to the leaves. The performed registration allows to determine intra- as well as inter-series spatiotemporal correspondences. Trichomes are localized by first detecting candidates using Hough transform. Then, local 3D invariants are extracted and the candidates are validated using a Support Vector Machine (SVM).
ISBN:1424439310
9781424439317
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2009.5193154