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.
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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
Other information
Original 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 1082-3654
Keywords (in Czech) automaticka mikroskopie, ostřicí funkce
Keywords in English automated microscopy; focus function
Tags automated microscopy, focus function
Tags International impact, Reviewed
Changed by Changed by: RNDr. Mgr. Silvie Luisa Brázdilová, Ph.D., učo 4123. Changed: 31/7/2010 22:34.
Abstract
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.
Abstract (in Czech)
Tato práce popisuje nový adaptivní ostřicí algoritmus pro multimodální ostřicí funkce v automatizované fluorescenční mikroskopii.
Links
LC535, research and development projectName: 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 projectName: 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
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