Hostname: page-component-848d4c4894-4rdrl Total loading time: 0 Render date: 2024-06-17T01:38:19.745Z Has data issue: false hasContentIssue false

Blind Deconvolution of Low and High Signal-to-noise 3-D Images of Fluorescent Subcellular Structures

Published online by Cambridge University Press:  02 July 2020

B. Rajwa
Affiliation:
Laboratory of Confocal Microscopy and Image Analysis, Department of Biophysics, 31-120, Krakow, Poland
J. Czyz
Affiliation:
Department of Cell Biology, Inst, of Molecular Biology, Jagiellonian University, 31-120, Krakow, Poland
J. Dobrucki
Affiliation:
Laboratory of Confocal Microscopy and Image Analysis, Department of Biophysics, 31-120, Krakow, Poland
Get access

Extract

This report summarizes our experiences gained with deconvolving 3-d images of various types of biological samples - animal and plant cells; live and fixed. We compared raw, unmodified images with images processed with standard filtering methods and images improved by using deconvolution. The blind decovolution software package from AutoQuant, Inc. was used. All images were acquired using a BioRad MRC 1024 confocal microscope (attached to a Nikon Diaphot microscope), using fluorescence or reflected light. For thin (up to 25 μm) flat specimens a 60x PlanApo NA1.4 lens was used while for thick (25-200 μm) water containing samples a 40x Fluor NA1.15 water immersion lens was used.

In an attempt to test the usefulness and validity of deconvolution for various types of biological samples we adopted the following approaches:

1. collect images of structures that are known to be both fluorescent and reflective - compare the results of deconvolution of fluorescence images with reflected light images that are not deconvolved.

Type
Deconvolution of Biological Images for 3D Light Microscopy—Confocal & Widefield
Copyright
Copyright © Microscopy Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)