Detailed Information on Publication Record
2009
Information Content Analysis in Automated Microscopy Imaging using an Adaptive Autofocus Algorithm for Multimodal Functions
BRÁZDILOVÁ, Silvie Luisa and Michal KOZUBEKBasic information
Original name
Information Content Analysis in Automated Microscopy Imaging using an Adaptive Autofocus Algorithm for Multimodal Functions
Name in Czech
Analýza informačního obsahu v automatizované mikroskopii pomocí adaptivního samozaostřovacího algoritmu pro multimodální fuknce
Authors
BRÁZDILOVÁ, Silvie Luisa (203 Czech Republic, belonging to the institution) and Michal KOZUBEK (203 Czech Republic, guarantor, belonging to the institution)
Edition
Journal of Microscopy, Oxford, Blackwell Science, 2009, 0022-2720
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
20200 2.2 Electrical engineering, Electronic engineering, Information engineering
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
není předmětem státního či obchodního tajemství
Impact factor
Impact factor: 1.612
RIV identification code
RIV/00216224:14330/09:00035554
Organization unit
Faculty of Informatics
UT WoS
000271974200005
Keywords in English
automated microscopy; information content analysis; autofocusing; normalized variance
Tags
Tags
International impact, Reviewed
Změněno: 10/3/2018 16:43, prof. RNDr. Michal Kozubek, Ph.D.
V originále
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 on-line so that it can decide to use a bigger or smaller step size to acquire the next image. Therefore, the algorithm needs to know about typical behaviour of content functions. We show that for Normalized Variance, one of the most promising content functions, this knowledge can be obtained after normalizing with respect to the theoretical maximum of this function, and using hierarchical clustering. The resulting algorithm is more reliable and efficient than a simple procedure with constant steps.
In Czech
Práce se věnuje analýze informačního obsahu v automatizované mikroskopii pomocí adaptivního samozaostřovacího algoritmu pro multimodální fuknce
Links
| LC535, research and development project |
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| MSM0021622419, plan (intention) |
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| 2B06052, research and development project |
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