@InProceedings{7362464,
  author = {S. Nie and J. Yu and P. Chen and J. Zhang and Y. Wang},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {A novel method with a deep network and directional edges for automatic detection of a fetal head},
  year = {2015},
  pages = {654-658},
  abstract = {In this paper, we propose a novel method for the automatic detection of fetal head in 2D ultrasound images. Fetal head detection has been a challenging task, as the ultrasound images usually have poor quality, the structures contained in the images are complex, and the gray scale distribution is highly variable. Our approach is based on a deep belief network and a modified circle detection method. The whole process can be divided into two steps: first, a deep learning architecture is applied to search the whole image and determine the result patch that contains the entire fetal head; second, a modified circle detection method is used along with Hough transform to detect the position and size of the fetal head. In order to validate our method, experiments are performed on both synthetic data and clinic ultrasound data. A good performance of the proposed method is shown in the paper.},
  keywords = {belief networks;Hough transforms;medical signal detection;ultrasonic imaging;deep network;directional edges;automatic detection;2D ultrasound images;fetal head detection;gray scale distribution;deep belief network;modified circle detection;deep learning architecture;Hough transform;synthetic data;clinic ultrasound data;Head;Image edge detection;Ultrasonic imaging;Magnetic heads;Training;Europe;Signal processing;Fetal head;deep learning;circle detection},
  doi = {10.1109/EUSIPCO.2015.7362464},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570096689.pdf},
}

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Fetal head detection has been a challenging task, as the ultrasound images usually have poor quality, the structures contained in the images are complex, and the gray scale distribution is highly variable. Our approach is based on a deep belief network and a modified circle detection method. The whole process can be divided into two steps: first, a deep learning architecture is applied to search the whole image and determine the result patch that contains the entire fetal head; second, a modified circle detection method is used along with Hough transform to detect the position and size of the fetal head. In order to validate our method, experiments are performed on both synthetic data and clinic ultrasound data. 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Wang},\n booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},\n title = {A novel method with a deep network and directional edges for automatic detection of a fetal head},\n year = {2015},\n pages = {654-658},\n abstract = {In this paper, we propose a novel method for the automatic detection of fetal head in 2D ultrasound images. Fetal head detection has been a challenging task, as the ultrasound images usually have poor quality, the structures contained in the images are complex, and the gray scale distribution is highly variable. Our approach is based on a deep belief network and a modified circle detection method. The whole process can be divided into two steps: first, a deep learning architecture is applied to search the whole image and determine the result patch that contains the entire fetal head; second, a modified circle detection method is used along with Hough transform to detect the position and size of the fetal head. In order to validate our method, experiments are performed on both synthetic data and clinic ultrasound data. 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