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MAŠEK, J. BURGET, R. KARÁSEK, J. UHER, V. GÜNEY, S.
Original Title
Evolutionary Improved Object Detector for Ultrasound Images
Type
conference paper
Language
English
Original Abstract
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.
Keywords
carotid artery, genetic algorithms, ultrasound, object detection, Viola–Jones detector.
Authors
MAŠEK, J.; BURGET, R.; KARÁSEK, J.; UHER, V.; GÜNEY, S.
RIV year
2013
Released
2. 7. 2013
ISBN
978-1-4799-0402-0
Book
36th International Conference on Telecommunications and Signal processing
Pages from
586
Pages to
590
Pages count
5
BibTex
@inproceedings{BUT100842, author="Jan {Mašek} and Radim {Burget} and Jan {Karásek} and Václav {Uher} and Selda {Güney}", title="Evolutionary Improved Object Detector for Ultrasound Images", booktitle="36th International Conference on Telecommunications and Signal processing", year="2013", pages="586--590", doi="10.1109/TSP.2013.6614002", isbn="978-1-4799-0402-0" }