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
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 . These two pre-filters were proven to be applicable for denoising cellular tomography , . 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. "
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.The International Conference on Computer Graphics, Multimedia and Image Processing (CGMIP2014), Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia; 11/2014
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- ": cryoET for the study of actin–ABP complexes CryoET has already been used extensively for the study of actin filaments and networks, but no studies of actin–ABP interactions were found. The Hanein lab has performed numerous studies of actin using cryoET and written several good reviews on the subject   "
ABSTRACT: Actin is a multifunctional eukaryotic protein with a globular monomer form that polymerizes into a thin, linear microfilament in cells. Through interactions with various actin-binding proteins (ABPs), actin plays an active role in many cellular processes, such as cell motility and structure. Microscopy techniques are powerful tools for determining the role and mechanism of actin-ABP interactions in these processes. In this article, we describe the basic concepts of fluorescence speckle microscopy, total internal reflection fluorescence microscopy, atomic force microscopy, and cryo-electron microscopy and review recent studies that utilize these techniques to visualize the binding of actin with ABPs.Analytical Biochemistry 09/2013; 443(2). DOI:10.1016/j.ab.2013.09.008 · 2.22 Impact Factor
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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. "
ABSTRACT: The influenza A virus genome consists of eight viral RNAs (vRNAs) that form viral ribonucleoproteins (vRNPs). Even though evidence supporting segment-specific packaging of vRNAs is accumulating, the mechanism ensuring selective packaging of one copy of each vRNA into the viral particles remains largely unknown. We used electron tomography to show that the eight vRNPs emerge from a common 'transition zone' located underneath the matrix layer at the budding tip of the virions, where they appear to be interconnected and often form a star-like structure. This zone appears as a platform in 3D surface rendering and is thick enough to contain all known packaging signals. In vitro, all vRNA segments are involved in a single network of intermolecular interactions. The regions involved in the strongest interactions were identified and correspond to known packaging signals. A limited set of nucleotides in the 5' region of vRNA 7 was shown to interact with vRNA 6 and to be crucial for packaging of the former vRNA. Collectively, our findings support a model in which the eight genomic RNA segments are selected and packaged as an organized supramolecular complex held together by direct base pairing of the packaging signals.Nucleic Acids Research 11/2011; 40(5):2197-209. DOI:10.1093/nar/gkr985 · 9.11 Impact Factor
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