Evolutionary Improved Object Detector for Ultrasound Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F13%3APU104508" target="_blank" >RIV/00216305:26220/13:PU104508 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TSP.2013.6614002" target="_blank" >http://dx.doi.org/10.1109/TSP.2013.6614002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2013.6614002" target="_blank" >10.1109/TSP.2013.6614002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary Improved Object Detector for Ultrasound Images

Popis výsledku v původním jazyce

Object detection in ultrasound images is difficult problem mainly because of relatively low signal–to–noise ratio. This paper deals with object detection in the noisy ultrasound images using modified version of Viola–Jones object detector. The method describes detection of carotid artery longitudinal section in ultrasound B–mode images. The detector is primarily trained by AdaBoost algorithm and uses a cascade of Haar–like features as a classifier. The main contribution of this paper is a method for detection of carotid artery longitudinal section. This method creates cascade of classifiers automatically using genetic algorithms. We also created post–processing method that marks position of artery in the image. The proposed method was released as open–source software. Resulting detector achieved accuracy 96.29%. When compared to SVM classification enlarged with RANSAC (RANdom SAmple Consensus) method that was used for detection of carotid artery longitudinal section, works our method real–time.

Název v anglickém jazyce

Evolutionary Improved Object Detector for Ultrasound Images

Popis výsledku anglicky

Object detection in ultrasound images is difficult problem mainly because of relatively low signal–to–noise ratio. This paper deals with object detection in the noisy ultrasound images using modified version of Viola–Jones object detector. The method describes detection of carotid artery longitudinal section in ultrasound B–mode images. The detector is primarily trained by AdaBoost algorithm and uses a cascade of Haar–like features as a classifier. The main contribution of this paper is a method for detection of carotid artery longitudinal section. This method creates cascade of classifiers automatically using genetic algorithms. We also created post–processing method that marks position of artery in the image. The proposed method was released as open–source software. Resulting detector achieved accuracy 96.29%. When compared to SVM classification enlarged with RANSAC (RANdom SAmple Consensus) method that was used for detection of carotid artery longitudinal section, works our method real–time.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/FR-TI4%2F151" target="_blank" >FR-TI4/151: Výzkum a vývoj technologie pro detekci emocí v nestrukturovaných datech</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

36th International Conference on Telecommunications and Signal processing

ISBN

978-1-4799-0402-0

ISSN

—

e-ISSN

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Počet stran výsledku

5

Strana od-do

586-590

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Rome

Datum konání akce

2. 7. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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