Efficient automatic noise reduction of electron tomographic reconstructions based on iterative median filtering

The Burnham Institute of Medical Research, La Jolla, CA 92037, USA.
Journal of Structural Biology (Impact Factor: 3.23). 06/2007; 158(2):196-204. DOI: 10.1016/j.jsb.2006.10.030
Source: PubMed


A simple, fast and efficient noise-reduction protocol for three-dimensional electron tomographic reconstructions of biological material is presented. The approach is based on iterative application of median filtering and shows promise for automatic noise reduction as a pre-processor for automated data analysis tools which aim at segmentation, feature extraction and pattern recognition. The application of this algorithm produces encouraging results for a wide variety of experimental and synthetic electron tomographic reconstructions.

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    • "Pre-filtering is an effective way to denoise the images prior to segmentation – in particular 3D median filter and non-linear anisotropic diffusion filter [10]. These two pre-filters were proven to be applicable for denoising cellular tomography [10], [13]. Based on the method descriptions, they could significantly improve the signal-to-noise ratios (SNR) of these ET images prior to organelle segmentation, thereby improving the quality of the latter. "
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    ABSTRACT: Electron tomography (ET) is a powerful tool for quantitatively mapping the complex 3D sub-cellular structures of cells. High accuracy segmentation results are great value to cell biologists. They facilitate the comparison processes across statistically significant datasets of properties or structure information like size/volume of cellular compartments. Manual segmentation is reasonably accurate but the process might be too slow – since the accuracy is highly dependent on the training of the person conducting the task. Automated segmentation therefore opens a number of opportunities. But these automated methods must be fast and capable of accurately delineating all contours of interest, ideally at organelle and molecular level – where many of which were reportedly not successful on ET datasets. Semi-automated approaches however have substantially allowed wider scope in resulting maintained cellular membrane tracing quality and accuracy and providing improved segmentation time. These reasons have motivated the development of a pipeline – semi-automated cellular tomogram segmentation workflow (CTSW) with particular components – that will find the best settings of chosen combination methods for high resolution tomogram segmentation specific to the intrinsic properties of the image volume being processed. The study also introduced a set of scoring objectives to enable timely segmentation of cellular compartments and expedite the process of optimizing method settings.
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    • "The final tomograms were calculated by weighted backprojection. Individual virions were extracted from the calculated tomograms and denoised using an iterative median filter (25). The 3D volumes were imported into the 3D visualization software AMIRA (Mercury Computer Systems, Mérignac, France) for visualization, manual segmentation and surface rendering. "
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