"Object detection in ultrasound images is difficult problem mainly because of relatively low signal\u2013to\u2013noise ratio. This paper deals with object detection in the noisy ultrasound images using modified version of Viola\u2013Jones object detector. The method describes detection of carotid artery longitudinal section in ultrasound B\u2013mode images. The detector is primarily trained by AdaBoost algorithm and uses a cascade of Haar\u2013like 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\u2013processing method that marks position of artery in the image. The proposed method was released as open\u2013source 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 rea" . "G\u00FCney, Selda" . "Kar\u00E1sek, Jan" . . . "Uher, V\u00E1clav" . . "RIV/00216305:26220/13:PU104508" . "73822" . . "Evolutionary Improved Object Detector for Ultrasound Images"@en . "10.1109/TSP.2013.6614002" . . "Rome" . "Burget, Radim" . . "Ma\u0161ek, Jan" . . "2013-07-02+02:00"^^ . . . . "Neuveden" . . . "978-1-4799-0402-0" . . "5"^^ . . . "Evolutionary Improved Object Detector for Ultrasound Images"@en . "5"^^ . . . . . . "Evolutionary Improved Object Detector for Ultrasound Images" . "P(FR-TI4/151), S" . "36th International Conference on Telecommunications and Signal processing" . "Neuveden" . . "[E45A30340BC5]" . "26220" . "Evolutionary Improved Object Detector for Ultrasound Images" . "Object detection in ultrasound images is difficult problem mainly because of relatively low signal\u2013to\u2013noise ratio. This paper deals with object detection in the noisy ultrasound images using modified version of Viola\u2013Jones object detector. The method describes detection of carotid artery longitudinal section in ultrasound B\u2013mode images. The detector is primarily trained by AdaBoost algorithm and uses a cascade of Haar\u2013like 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\u2013processing method that marks position of artery in the image. The proposed method was released as open\u2013source 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 rea"@en . . "4"^^ . "RIV/00216305:26220/13:PU104508!RIV14-MPO-26220___" . . "carotid artery, genetic algorithms, ultrasound, object detection, Viola\u2013Jones detector."@en .