Information content analysis in automated microscopy imaging using an adaptive autofocus algorithm for multimodal functions

Journal of Microscopy
S L Brázdilová, M Kozubek

Abstract

We present a new algorithm to analyse information content in images acquired using automated fluorescence microscopy. The algorithm belongs to the group of autofocusing methods, but differs from its predecessors in that it can handle thick specimens and operate also in confocal mode. It measures the information content in images using a 'content function', which is essentially the same concept as a focus function. Unlike previously presented algorithms, this algorithm tries to find all significant axial positions in cases where the content function applied to real data is not unimodal, which is often the case. This requirement precludes using algorithms that rely on unimodality. Moreover, choosing a content function requires careful consideration, because some functions suppress local maxima. First, we test 19 content functions and evaluate their ability to show local maxima clearly. The results show that only six content functions succeed. To save time, the acquisition procedure needs to vary the step size adaptively, because a wide range of possible axial positions has to be passed so as not to miss a local maximum. The algorithm therefore has to assess the steepness of the content function online so that it can decide to use...Continue Reading

References

Jan 1, 1991·Cytometry·L FirestoneK Preston
Feb 5, 1998·Journal of Microscopy·A SantosF del Pozo
Dec 18, 2004·Microscopy Research and Technique·Yu SunBradley J Nelson
May 17, 2005·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Vincenzo Della MeaCarlo A Beltrami

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Citations

Mar 11, 2015·Microscopy Research and Technique·Chao-Chen GuXin-Ping Guan
Jun 25, 2020·Sensors·Chia-Ming JanJyun-Yi Yang

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