DocumentCode :
3001670
Title :
Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information
Author :
Shaolei Feng ; Zhou, S. Kevin ; Good, Sara ; Comaniciu, Dorin
Author_Institution :
Corp. Res., Integrated Data Syst. Dept., Siemens, Princeton, NJ, USA
fYear :
2009
fDate :
20-25 June 2009
Firstpage :
2488
Lastpage :
2495
Abstract :
3D ultrasound imaging has been increasingly used in clinics for fetal examination. However, manually searching for the optimal view of the fetal face in 3D ultrasound volumes is cumbersome and time-consuming even for expert physicians and sonographers. In this paper we propose a learning-based approach which combines both 3D and 2D information for automatic and fast fetal face detection from 3D ultrasound volumes. Our approach applies a new technique - constrained marginal space learning - for 3D face mesh detection, and combines a boosting-based 2D profile detection to refine 3D face pose. To enhance the rendering of the fetal face, an automatic carving algorithm is proposed to remove all obstructions in front of the face based on the detected face mesh. Experiments are performed on a challenging 3D ultrasound data set containing 1010 fetal volumes. The results show that our system not only achieves excellent detection accuracy but also runs very fast - it can detect the fetal face from the 3D data in 1 second on a dual-core 2.0 GHz computer.
Keywords :
biomedical ultrasonics; feature extraction; learning (artificial intelligence); medical image processing; 2D information; 2D profile detection; 3D face mesh detection; 3D face pose; 3D information; 3D ultrasound imaging; 3D ultrasound volumes; automatic carving; automatic fetal face detection; constrained marginal space learning; fetal examination; learning based approach; Biomedical imaging; Computed tomography; Computer vision; Eyes; Face detection; Fetus; Magnetic resonance imaging; Navigation; Nose; Ultrasonic imaging;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
ISSN :
1063-6919
Print_ISBN :
978-1-4244-3992-8
Type :
conf
DOI :
10.1109/CVPR.2009.5206527
Filename :
5206527
Link To Document :
بازگشت