Automated Segmentation and Object Classification of CT Images : Application to In Vivo Molecular Imaging of Avian Embryos

Joint Authors

Zimmermann, Johannes
Schmidt, Jana
Heidrich, Alexander
Saluz, Hans Peter

Source

International Journal of Biomedical Imaging

Issue

Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2013-08-12

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Medicine

Abstract EN

Background.

Although chick embryogenesis has been studied extensively, there has been growing interest in the investigation of skeletogenesis.

In addition to improved poultry health and minimized economic loss, a greater understanding of skeletal abnormalities can also have implications for human medicine.

True in vivo studies require noninvasive imaging techniques such as high-resolution microCT.

However, the manual analysis of acquired images is both time consuming and subjective.

Methods.

We have developed a system for automated image segmentation that entails object-based image analysis followed by the classification of the extracted image objects.

For image segmentation, a rule set was developed using Definiens image analysis software.

The classification engine was implemented using the WEKA machine learning tool.

Results.

Our system reduces analysis time and observer bias while maintaining high accuracy.

Applying the system to the quantification of long bone growth has allowed us to present the first true in ovo data for bone length growth recorded in the same chick embryos.

Conclusions.

The procedures developed represent an innovative approach for the automated segmentation, classification, quantification, and visualization of microCT images.

MicroCT offers the possibility of performing longitudinal studies and thereby provides unique insights into the morpho- and embryogenesis of live chick embryos.

American Psychological Association (APA)

Heidrich, Alexander& Schmidt, Jana& Zimmermann, Johannes& Saluz, Hans Peter. 2013. Automated Segmentation and Object Classification of CT Images : Application to In Vivo Molecular Imaging of Avian Embryos. International Journal of Biomedical Imaging،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-477225

Modern Language Association (MLA)

Heidrich, Alexander…[et al.]. Automated Segmentation and Object Classification of CT Images : Application to In Vivo Molecular Imaging of Avian Embryos. International Journal of Biomedical Imaging No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-477225

American Medical Association (AMA)

Heidrich, Alexander& Schmidt, Jana& Zimmermann, Johannes& Saluz, Hans Peter. Automated Segmentation and Object Classification of CT Images : Application to In Vivo Molecular Imaging of Avian Embryos. International Journal of Biomedical Imaging. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-477225

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-477225