An Adaptive Algorithm for Multimodal Focus Functions in Automated Fluorescence Microscopy

BRÁZDILOVÁ, Silvie Luisa and Michal KOZUBEK. An Adaptive Algorithm for Multimodal Focus Functions in Automated Fluorescence Microscopy. In Medical Imaging Conference. Dresden: IEEE, 2008, p. 1-5. ISBN 978-1-4244-2714-7.

Basic information

Original name

An Adaptive Algorithm for Multimodal Focus Functions in Automated Fluorescence Microscopy

Name in Czech

Adaptivní algoritmus pro multimodální ostřicí funkce v automatizované fluorescenční mikroskopii

Authors

BRÁZDILOVÁ, Silvie Luisa (203 Czech Republic) and Michal KOZUBEK (203 Czech Republic, guarantor)

Edition

Dresden, Medical Imaging Conference, p. 1-5, 2008

Publisher

IEEE

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Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

20200 2.2 Electrical engineering, Electronic engineering, Information engineering

Country of publisher

Germany

Confidentiality degree

is not subject to a state or trade secret

RIV identification code

RIV/00216224:14330/08:00026789

Organization unit

Faculty of Informatics

ISBN

978-1-4244-2714-7

ISSN

Keywords (in Czech)

automaticka mikroskopie, ostřicí funkce

Keywords in English

automated microscopy; focus function

Tags

automated microscopy, focus function

Tags

International impact, Reviewed

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Abstract

In the original language

This work presents a new autofocusing algorithm for fluorescence microscopy that aims at finding all significant planes of focus in cases that the focus function applied on real data is not unimodal, which is often the case. First, nineteen focus functions are tested and their ability to show local maxima clearly is evaluated. The results show that only six focus functions work successfully. Then adaptively variable step size is introduced because wide range of possible focus positions has to be passed not to miss a local maximum. The algorithm therefore assesses the steepness of the focus function on-line so that it can decide whether bigger or smaller step size should be used for acquiring next image. It is shown that for Normalized Variance, the knowledge about steepness can be obtained after normalizing with respect to the theoretical maximum of this function. The resulting algorithm is reliable and efficient compared to a simple procedure with constant steps.

In Czech

Tato práce popisuje nový adaptivní ostřicí algoritmus pro multimodální ostřicí funkce v automatizované fluorescenční mikroskopii.

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Links

LC535, research and development project	Name: Dynamika a organizace chromosomů během buněčného cyklu v normě a patologii Investor: Ministry of Education, Youth and Sports of the CR, Dynamika a organizace chromosomů během buněčného cyklu v normě a patologii
MSM0021622419, plan (intention)	Name: Vysoce paralelní a distribuované výpočetní systémy Investor: Ministry of Education, Youth and Sports of the CR, Highly Parallel and Distributed Computing Systems
2B06052, research and development project	Name: Vytipování markerů, screening a časná diagnostika nádorových onemocnění pomocí vysoce automatizovaného zpracování multidimenzionálních biomedicínských obrazů (Acronym: Biomarker) Investor: Ministry of Education, Youth and Sports of the CR, Determination of markers, screening and early diagnostics of cancer diseases using highly automated processing of multidimensional biomedical images