TY - GEN
T1 - Automatic detection and measurement of fetal biometrics to determine the Gestational Age
AU - Imaduddin, Zaki
AU - Akbar, Muhammad Ali
AU - Tawakal, Hilmy Abidzar
AU - Satwika, I. Putu
AU - Saroyo, Yudianto Budi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/31
Y1 - 2015/8/31
N2 - An Ultrasonography (USG) feature extraction and classification system has been developed for detecting and analyzing organ structure in fetal body. This study aims to detect and measure features of fetus from scanned image of fetal head (biparietal diameter) and femur length. We propose Haar-like feature is used to extract feature from cropping image object, meanwhile AdaBoost classifier is used for object detection and Randomized Hough Transform is applied for biometry measurement. In this research, we used 300 biparietal head image data and 200 image data of femur. After the data processing stage, we obtained the detection and measurement of biparietal as many as 44 images with an average error of 0.039 and Correlation Coefficient result of 0.984, while the results for the detection and measurement of fetal femur error as many as 18 with an average of 0.101 and Correlation Coefficient result of 0.763. Predicting Gestational Age(GA) using our method are match with manually predicted GA, average error of predicting GA with BPD is 0.005, and with femur length is 0.033. The result of this research can be optimized further to realize a fully integrated system that can detect and measure fetal organ with usable user interaction and affordable price.
AB - An Ultrasonography (USG) feature extraction and classification system has been developed for detecting and analyzing organ structure in fetal body. This study aims to detect and measure features of fetus from scanned image of fetal head (biparietal diameter) and femur length. We propose Haar-like feature is used to extract feature from cropping image object, meanwhile AdaBoost classifier is used for object detection and Randomized Hough Transform is applied for biometry measurement. In this research, we used 300 biparietal head image data and 200 image data of femur. After the data processing stage, we obtained the detection and measurement of biparietal as many as 44 images with an average error of 0.039 and Correlation Coefficient result of 0.984, while the results for the detection and measurement of fetal femur error as many as 18 with an average of 0.101 and Correlation Coefficient result of 0.763. Predicting Gestational Age(GA) using our method are match with manually predicted GA, average error of predicting GA with BPD is 0.005, and with femur length is 0.033. The result of this research can be optimized further to realize a fully integrated system that can detect and measure fetal organ with usable user interaction and affordable price.
KW - AdaBoost
KW - Randomize Hough Transform(RHT)
KW - Ultrasonography (USG)
UR - http://www.scopus.com/inward/record.url?scp=84960509959&partnerID=8YFLogxK
U2 - 10.1109/ICoICT.2015.7231495
DO - 10.1109/ICoICT.2015.7231495
M3 - Conference contribution
AN - SCOPUS:84960509959
T3 - 2015 3rd International Conference on Information and Communication Technology, ICoICT 2015
SP - 608
EP - 612
BT - 2015 3rd International Conference on Information and Communication Technology, ICoICT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Information and Communication Technology, ICoICT 2015
Y2 - 27 May 2015 through 29 May 2015
ER -