title: A novel 3D wavelet-based filter for visualizing features in noisy biological data.
abstract: Summary We have developed a three-dimensional (3D) wavelet-based filter for visualizing structural features in volumetric data. The only variable parameter is a characteristic linear size of the feature of interest. The filtered output contains only those regions that are correlated with the characteristic size, thus de-noising the image. We demonstrate the use of the filter by applying it to 3D data from a variety of electron microscopy samples, including low-contrast vitreous ice cryogenic preparations, as well as 3D optical microscopy specimens.
authors.affiliation: Lawrence Livermore National Laboratory, CA 94550, USA. wmoss@llnl.gov
authors.forename: W C
authors.initials: WC
authors.lastname: Moss
authors.forename: S
authors.initials: S
authors.lastname: Haase
authors.forename: J M
authors.initials: JM
authors.lastname: Lyle
authors.forename: D A
authors.initials: DA
authors.lastname: Agard
authors.forename: J W
authors.initials: JW
authors.lastname: Sedat
mesh_terms: D000818:Animals; D002502:Centrioles / Q000648:ultrastructure; D020285:Cryoelectron Microscopy / Q000379:methods; D005057:Eukaryotic Cells / Q000648:ultrastructure; D005374:Filtration; D008433:Mathematics; D008853:Microscopy / Q000379:methods*; D008870:Microtubules / Q000648:ultrastructure; D014054:Tomography / Q000379:methods; D014404:Tubulin / Q000648:ultrastructure
chemical_list: D014404:Tubulin