Evolutionary Improved Object Detector for Ultrasound Images
36th International Conference on Telecommunications and Signal Processing (TSP), Rome, İtalya, 2 - 04 Temmuz 2013, ss.586-590, (Tam Metin Bildiri)
- 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.