Abstract
A novel learning based automatic method is proposed to detect the fetal head for the measurement of head circumference from ultrasound images. We first exploit the AdaBoost learning method to train the classifier on Haar-like features and then, for the first time, we propose to use prior knowledge and online imaging parameters to guide the sliding window based head detection from ultrasound images. This approach can significantly improve both detection rate and speed. The boundary of the head in the localized region is further detected using a local phase based method, which is insensitive to speckle noises and intensity changes in ultrasound images. Finally iterative randomized Hough transform (IRHT) is employed to determine an ellipse on the head contour. Experiments performed on 675 images (500 for classifier training and 175 for measurement) showed that mean-signed-difference between automatic and manual measurements is 2.86 mm (1.6%). The statistical analysis further indicated that there was no significant difference between these two measurements. These results demonstrated the proposed fully automatic framework can be used as a consistent and accurate tool in clinical practice.
Original language | English |
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Title of host publication | ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging |
Subtitle of host publication | From Nano to Macro |
Pages | 772-775 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 22 Aug 2013 |
Externally published | Yes |
Event | 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States Duration: 7 Apr 2013 → 11 Apr 2013 |
Conference
Conference | 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 |
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Country/Territory | United States |
City | San Francisco, CA |
Period | 7/04/13 → 11/04/13 |
Keywords
- AdaBoost
- Biometry
- Fetal ultrasound
- Hough transform
- Local phase information
- Prior knowledge and imaging parameters
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging