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An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms.

Publication ,  Journal Article
Bartesaghi, A; Sapiro, G; Subramaniam, S
Published in: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
September 2005

Electron tomography allows for the determination of the three-dimensional structures of cells and tissues at resolutions significantly higher than that which is possible with optical microscopy. Electron tomograms contain, in principle, vast amounts of information on the locations and architectures of large numbers of subcellular assemblies and organelles. The development of reliable quantitative approaches for the analysis of features in tomograms is an important problem, and a challenging prospect due to the low signal-to-noise ratios that are inherent to biological electron microscopic images. This is, in part, a consequence of the tremendous complexity of biological specimens. We report on a new method for the automated segmentation of HIV particles and selected cellular compartments in electron tomograms recorded from fixed, plastic-embedded sections derived from HIV-infected human macrophages. Individual features in the tomogram are segmented using a novel robust algorithm that finds their boundaries as global minimal surfaces in a metric space defined by image features. The optimization is carried out in a transformed spherical domain with the center an interior point of the particle of interest, providing a proper setting for the fast and accurate minimization of the segmentation energy. This method provides tools for the semi-automated detection and statistical evaluation of HIV particles at different stages of assembly in the cells and presents opportunities for correlation with biochemical markers of HIV infection. The segmentation algorithm developed here forms the basis of the automated analysis of electron tomograms and will be especially useful given the rapid increases in the rate of data acquisition. It could also enable studies of much larger data sets, such as those which might be obtained from the tomographic analysis of HIV-infected cells from studies of large populations.

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Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

September 2005

Volume

14

Issue

9

Start / End Page

1314 / 1323

Related Subject Headings

  • Tomography
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Microscopy, Electron, Scanning
  • Imaging, Three-Dimensional
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • HIV
  • Electrons
 

Citation

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ICMJE
MLA
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Bartesaghi, A., Sapiro, G., & Subramaniam, S. (2005). An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 14(9), 1314–1323. https://doi.org/10.1109/tip.2005.852467
Bartesaghi, Alberto, Guillermo Sapiro, and Sriram Subramaniam. “An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society 14, no. 9 (September 2005): 1314–23. https://doi.org/10.1109/tip.2005.852467.
Bartesaghi A, Sapiro G, Subramaniam S. An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2005 Sep;14(9):1314–23.
Bartesaghi, Alberto, et al. “An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, vol. 14, no. 9, Sept. 2005, pp. 1314–23. Epmc, doi:10.1109/tip.2005.852467.
Bartesaghi A, Sapiro G, Subramaniam S. An energy-based three-dimensional segmentation approach for the quantitative interpretation of electron tomograms. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2005 Sep;14(9):1314–1323.

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

September 2005

Volume

14

Issue

9

Start / End Page

1314 / 1323

Related Subject Headings

  • Tomography
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Microscopy, Electron, Scanning
  • Imaging, Three-Dimensional
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • HIV
  • Electrons