Evolutionary Improved Object Detector for Ultrasound Images

Masek J., Burget R., Karasek J., Uher V., Guney S.

36th International Conference on Telecommunications and Signal Processing (TSP), Rome, İtalya, 2 - 04 Temmuz 2013, ss.586-590 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/tsp.2013.6614002
  • Basıldığı Şehir: Rome
  • Basıldığı Ülke: İtalya
  • Sayfa Sayıları: ss.586-590
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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.