Random Forests Pixel-wise Classification for Detection and Segmentation of Cells in the Images from Holographic Microscope

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F17%3APU124471" target="_blank" >RIV/00216305:26220/17:PU124471 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.radio.feec.vutbr.cz/ieee/userfiles/downloads/archive/2017-Mikulov/Proceedings_Mikulov_2017.pdf" target="_blank" >http://www.radio.feec.vutbr.cz/ieee/userfiles/downloads/archive/2017-Mikulov/Proceedings_Mikulov_2017.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Random Forests Pixel-wise Classification for Detection and Segmentation of Cells in the Images from Holographic Microscope

Popis výsledku v původním jazyce

Microscopic cell image analysis is widely used by biologists for cell behavior and cell morphology study. In dense cell cultures precise single-cell segmentation is challenging task and it is an important step for automatic cell analysis methods. This work introduces a novel method for robust single cell segmentation of images from holographic microscope. The method is based on pixel-wise classification with random forests for both background segmentation a cell detection, where cell detection image is refined with distance transform based detector. Final single cell segmentation combines both detection and background with seeded watershed. Proposed background segmentation part reaches results similar to other algorithms, but cell detection part of the algorithm is innovative and achieves significantly better result than commonly used detector.

Název v anglickém jazyce

Random Forests Pixel-wise Classification for Detection and Segmentation of Cells in the Images from Holographic Microscope

Popis výsledku anglicky

Microscopic cell image analysis is widely used by biologists for cell behavior and cell morphology study. In dense cell cultures precise single-cell segmentation is challenging task and it is an important step for automatic cell analysis methods. This work introduces a novel method for robust single cell segmentation of images from holographic microscope. The method is based on pixel-wise classification with random forests for both background segmentation a cell detection, where cell detection image is refined with distance transform based detector. Final single cell segmentation combines both detection and background with seeded watershed. Proposed background segmentation part reaches results similar to other algorithms, but cell detection part of the algorithm is innovative and achieves significantly better result than commonly used detector.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of IEEE Student Branch Conference Mikulov 2017

ISBN

978-80-214-5526-9

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

67-70

Název nakladatele

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Místo vydání

Brno

Místo konání akce

Mikulov, Czech republic

Datum konání akce

28. 8. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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