Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos
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 me...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2013/508474 |
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| _version_ | 1832560985669369856 |
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| author | Alexander Heidrich Jana Schmidt Johannes Zimmermann Hans Peter Saluz |
| author_facet | Alexander Heidrich Jana Schmidt Johannes Zimmermann Hans Peter Saluz |
| author_sort | Alexander Heidrich |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f92631f57d4a4e03b9a0d036106e8ee4 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-f92631f57d4a4e03b9a0d036106e8ee42025-02-03T01:26:15ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962013-01-01201310.1155/2013/508474508474Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian EmbryosAlexander Heidrich0Jana Schmidt1Johannes Zimmermann2Hans Peter Saluz3Department of Cell and Molecular Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Beutenberg Straße 11a, 07745 Jena, GermanyInstitut für Informatik/I12, Technische Universität München, Boltzmannstrraße 3, 85748 Garching bei München, GermanyDefiniens AG, Bernhard-Wicki-Straße 5, 80636 München, GermanyDepartment of Cell and Molecular Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Beutenberg Straße 11a, 07745 Jena, GermanyBackground. 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.http://dx.doi.org/10.1155/2013/508474 |
| spellingShingle | Alexander Heidrich Jana Schmidt Johannes Zimmermann Hans Peter Saluz Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos International Journal of Biomedical Imaging |
| title | Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos |
| title_full | Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos |
| title_fullStr | Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos |
| title_full_unstemmed | Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos |
| title_short | Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos |
| title_sort | automated segmentation and object classification of ct images application to in vivo molecular imaging of avian embryos |
| url | http://dx.doi.org/10.1155/2013/508474 |
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