PROCEEDINGS ARTICLE | June 14, 2006
Proc. SPIE. 6254, Seventh International Conference on Correlation Optics
KEYWORDS: Microscopes, Point spread functions, 3D acquisition, Image processing, Microscopy, Luminescence, Image acquisition, Deconvolution, Algorithm development, 3D image processing
3-D optical fluorescent microscopy now becomes an efficient tool for the volume investigation of living biological
samples. Developments in instrumentation have permitted to beat off the conventional Abbe limit. In any case the
recorded image can be described by the convolution equation between the original object and the Point Spread Function
(PSF) of the acquisition system. Due to the finite resolution of the instrument, the original object is recorded with
distortions and blurring, and contaminated by noise. This induces that relevant biological information cannot be
extracted directly from raw data stacks.
If the goal is 3-D quantitative analysis, then to assess optimal performance of the instrument and to ensure the data
acquisition reproducibility, the system characterization is mandatory. The PSF represents the properties of the image
acquisition system; we have proposed the use of statistical tools and Zernike moments to describe a 3-D PSF system and
to quantify the variation of the PSF. This first step toward standardization is helpful to define an acquisition protocol
optimizing exploitation of the microscope depending on the studied biological sample.
Before the extraction of geometrical information and/or intensities quantification, the data restoration is mandatory.
Reduction of out-of-focus light is carried out computationally by deconvolution process. But other phenomena occur
during acquisition, like fluorescence photo degradation named "bleaching", inducing an alteration of information
needed for restoration. Therefore, we have developed a protocol to pre-process data before the application of
deconvolution algorithms.
A large number of deconvolution methods have been described and are now available in commercial package. One
major difficulty to use this software is the introduction by the user of the "best" regularization parameters. We have
pointed out that automating the choice of the regularization level; also greatly improves the reliability of the
measurements although it facilitates the use. Furthermore, to increase the quality and the repeatability of quantitative
measurements a pre-filtering of images improves the stability of deconvolution process. In the same way, the PSF prefiltering
stabilizes the deconvolution process. We have shown that Zemike polynomials can be used to reconstruct
experimental PSF, preserving system characteristics and removing the noise contained in the PSF.
