Publication:
Robust membrane detection based on tensor voting for electron tomography

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Robust membrane detection.pdf(2.15 MB)
Date
2014-04
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Authors
Martinez-Sanchez, Antonio ; GarcĂ­a, Inmaculada ; Asano, Shoh ; Lucic, Valdan ; Fernandez, Jose-Jesus
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Publisher
Elsevier
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IngenierĂ­a de la InformaciĂłn y las Comunicaciones
DOI
https://doi.org/10.1016/j.jsb.2014.02.015
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info:eu-repo/semantics/article
Description
© 2014. Elsevier. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Journal of Structural Biology. To access the final edited and published work see https://doi.org/10.1016/j.jsb.2014.02.015
Abstract
Electron tomography enables three-dimensional (3D) visualization and analysis of the subcellular architecture at a resolution of a few nanometers. Segmentation of structural components present in 3D images (tomograms) is often necessary for their interpretation. However, it is severely hampered by a number of factors that are inherent to electron tomography (e.g. noise, low contrast, distortion). Thus, there is a need for new and improved computational methods to facilitate this challenging task. In this work, we present a new method for membrane segmentation that is based on anisotropic propagation of the local structural information using the tensor voting algorithm. The local structure at each voxel is then refined according to the information received from other voxels. Because voxels belonging to the same membrane have coherent structural information, the underlying global structure is strengthened. In this way, local information is easily integrated at a global scale to yield segmented structures. This method performs well under low signal-to-noise ratio typically found in tomograms of vitrified samples under cryo-tomography conditions and can bridge gaps present on membranes. The performance of the method is demonstrated by applications to tomograms of different biological samples and by quantitative comparison with standard template matching procedure.
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Segmentation , Image processing , Electron tomography , Membrane , Tensor voting , Steerable filters
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http://hdl.handle.net/10201/136083
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