Comparison of image reconstruction methods for structured illumination microscopy

Tomas Lukeš; Guy M. Hagen; Pavel Křížek; Zdeněk Švindrych; Karel Fliegel; Miloš Klíma

doi:10.1117/12.2052621

8 May 2014 Comparison of image reconstruction methods for structured illumination microscopy

Tomas Lukeš, Guy M. Hagen, Pavel Křížek, Zdeněk Švindrych, Karel Fliegel, Miloš Klíma

Proceedings Volume 9129, Biophotonics: Photonic Solutions for Better Health Care IV; 91293J (2014) https://doi.org/10.1117/12.2052621
Event: SPIE Photonics Europe, 2014, Brussels, Belgium

Abstract

Structured illumination microscopy (SIM) is a recent microscopy technique that enables one to go beyond the diffraction limit using patterned illumination. The high frequency information is encoded through aliasing into the observed image. By acquiring multiple images with different illumination patterns aliased components can be separated and a highresolution image reconstructed. Here we investigate image processing methods that perform the task of high-resolution image reconstruction, namely square-law detection, scaled subtraction, super-resolution SIM (SR-SIM), and Bayesian estimation. The optical sectioning and lateral resolution improvement abilities of these algorithms were tested under various noise level conditions on simulated data and on fluorescence microscopy images of a pollen grain test sample and of a cultured cell stained for the actin cytoskeleton. In order to compare the performance of the algorithms, the following objective criteria were evaluated: Signal to Noise Ratio (SNR), Signal to Background Ratio (SBR), circular average of the power spectral density and the S₃ sharpness index. The results show that SR-SIM and Bayesian estimation combine illumination patterned images more effectively and provide better lateral resolution in exchange for more complex image processing. SR-SIM requires one to precisely shift the separated spectral components to their proper positions in reciprocal space. High noise levels in the raw data can cause inaccuracies in the shifts of the spectral components which degrade the super-resolved image. Bayesian estimation has proven to be more robust to changes in noise level and illumination pattern frequency.

Citation Download Citation

Tomas Lukeš, Guy M. Hagen, Pavel Křížek, Zdeněk Švindrych, Karel Fliegel, and Miloš Klíma "Comparison of image reconstruction methods for structured illumination microscopy", Proc. SPIE 9129, Biophotonics: Photonic Solutions for Better Health Care IV, 91293J (8 May 2014); https://doi.org/10.1117/12.2052621

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