Information Content Analysis in Automated Microscopy Imaging using an Adaptive Autofocus Algorithm for Multimodal Functions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F09%3A00035554" target="_blank" >RIV/00216224:14330/09:00035554 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Information Content Analysis in Automated Microscopy Imaging using an Adaptive Autofocus Algorithm for Multimodal Functions

Popis výsledku v původním jazyce

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 andoperate 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 axialpositions 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.

Název v anglickém jazyce

Information Content Analysis in Automated Microscopy Imaging using an Adaptive Autofocus Algorithm for Multimodal Functions

Popis výsledku anglicky

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 andoperate 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 axialpositions 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Microscopy

ISSN

0022-2720

e-ISSN

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Svazek periodika

236

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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