Bioimaging ? Autothresholding and segmentation via neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F17%3A43895368" target="_blank" >RIV/60076658:12520/17:43895368 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/49777513:23520/17:43932945

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-56148-6_31#enumeration" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-56148-6_31#enumeration</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-56148-6_31" target="_blank" >10.1007/978-3-319-56148-6_31</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bioimaging ? Autothresholding and segmentation via neural networks

Popis výsledku v původním jazyce

Bioimaging, image segmentation, thresholding, and multivariate processing are helpful tools in analysis of series of images from many time lapse experiments. The different methods of mathematic, algorithmization and artificial intelligence could by modified, parametrized or adopted for single purpose case of completely different biological background (namely microorganisms, tissue cultures, aquaculture). However, most of the task is based on initial image segmentation, before features axtraction and comparison tasks are evaluated. In this article, we compare several of classical approaches in bioinformatical and biophysical cases with the neural network approach. The concept of neural network was adopted from the biological neural networks. Th networks need to be trained, however after the learning phase, they should be able to find one solution for various objects. The comparison of the methods is evaluated via error in segmentation according to the human supervisor.

Název v anglickém jazyce

Bioimaging ? Autothresholding and segmentation via neural networks

Popis výsledku anglicky

Bioimaging, image segmentation, thresholding, and multivariate processing are helpful tools in analysis of series of images from many time lapse experiments. The different methods of mathematic, algorithmization and artificial intelligence could by modified, parametrized or adopted for single purpose case of completely different biological background (namely microorganisms, tissue cultures, aquaculture). However, most of the task is based on initial image segmentation, before features axtraction and comparison tasks are evaluated. In this article, we compare several of classical approaches in bioinformatical and biophysical cases with the neural network approach. The concept of neural network was adopted from the biological neural networks. Th networks need to be trained, however after the learning phase, they should be able to find one solution for various objects. The comparison of the methods is evaluated via error in segmentation according to the human supervisor.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

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)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-319-56147-9

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

358-368

Název nakladatele

Springer Verlag

Místo vydání

Cham

Místo konání akce

Granada, Španělsko

Datum konání akce

26. 4. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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