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Automatic Detection of Fetal Head and Fetal Measurement on Birth Time Estimation

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@article{IJASEIT8594,
   author = {Riyanto Sigit and Khusnul Danny Rahayu and Putri Nadiyah and Heny Yuniarti and - Anwar},
   title = {Automatic Detection of Fetal Head and Fetal Measurement on Birth Time Estimation},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {2},
   year = {2020},
   pages = {447--454},
   keywords = {ultrasound; fetal biometry; learning-based; biparietal diameter; femur length; integral projection.},
   abstract = {

Fetal biometry in ultrasound (USG) is a routine activity that can be used to determine the gestational age of a baby. Accuracy is needed when measurements are made. However, the low quality of ultrasound images and manual measurement that takes a long time and give rise to many different variations of values from each doctor or sonographer. Thus the measurement results obtained are less accurate. From these problems, the development of automatic detection and measurement of the fetal head is needed. One of them is by using a learning-based system method that will carry out the training process using Haar training to get features. The training process with the Haar method uses positive image data as objects and negative images as background. Haar training data that have been obtained, then used to detect fetal head objects automatically. Detection results are then processed to separate the object from the background image, which is then carried out the segmentation process to obtain the fetal head and fetal femur. Then the segmentation method used is Integral Projection to get the fetal head circumference and Find Contour to get the fetal femur. The parameters used to determine gestational ages are biparietal diameter and femur length. Based on experiments that have been done, obtained an accuracy rate of 97.77% using the proposed method for estimating gestational age automatically. Measurements are obtained by comparing the results of the doctor's diagnosis using manual measurements on an ultrasound machine.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8594},    doi = {10.18517/ijaseit.10.2.8594} }

EndNote

%A Sigit, Riyanto
%A Rahayu, Khusnul Danny
%A Nadiyah, Putri
%A Yuniarti, Heny
%A Anwar, -
%D 2020
%T Automatic Detection of Fetal Head and Fetal Measurement on Birth Time Estimation
%B 2020
%9 ultrasound; fetal biometry; learning-based; biparietal diameter; femur length; integral projection.
%! Automatic Detection of Fetal Head and Fetal Measurement on Birth Time Estimation
%K ultrasound; fetal biometry; learning-based; biparietal diameter; femur length; integral projection.
%X 

Fetal biometry in ultrasound (USG) is a routine activity that can be used to determine the gestational age of a baby. Accuracy is needed when measurements are made. However, the low quality of ultrasound images and manual measurement that takes a long time and give rise to many different variations of values from each doctor or sonographer. Thus the measurement results obtained are less accurate. From these problems, the development of automatic detection and measurement of the fetal head is needed. One of them is by using a learning-based system method that will carry out the training process using Haar training to get features. The training process with the Haar method uses positive image data as objects and negative images as background. Haar training data that have been obtained, then used to detect fetal head objects automatically. Detection results are then processed to separate the object from the background image, which is then carried out the segmentation process to obtain the fetal head and fetal femur. Then the segmentation method used is Integral Projection to get the fetal head circumference and Find Contour to get the fetal femur. The parameters used to determine gestational ages are biparietal diameter and femur length. Based on experiments that have been done, obtained an accuracy rate of 97.77% using the proposed method for estimating gestational age automatically. Measurements are obtained by comparing the results of the doctor's diagnosis using manual measurements on an ultrasound machine.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8594 %R doi:10.18517/ijaseit.10.2.8594 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 2 %@ 2088-5334

IEEE

Riyanto Sigit,Khusnul Danny Rahayu,Putri Nadiyah,Heny Yuniarti and - Anwar,"Automatic Detection of Fetal Head and Fetal Measurement on Birth Time Estimation," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 2, pp. 447-454, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.2.8594.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sigit, Riyanto
AU  - Rahayu, Khusnul Danny
AU  - Nadiyah, Putri
AU  - Yuniarti, Heny
AU  - Anwar, -
PY  - 2020
TI  - Automatic Detection of Fetal Head and Fetal Measurement on Birth Time Estimation
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 2
Y2  - 2020
SP  - 447
EP  - 454
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - ultrasound; fetal biometry; learning-based; biparietal diameter; femur length; integral projection.
N2  - 

Fetal biometry in ultrasound (USG) is a routine activity that can be used to determine the gestational age of a baby. Accuracy is needed when measurements are made. However, the low quality of ultrasound images and manual measurement that takes a long time and give rise to many different variations of values from each doctor or sonographer. Thus the measurement results obtained are less accurate. From these problems, the development of automatic detection and measurement of the fetal head is needed. One of them is by using a learning-based system method that will carry out the training process using Haar training to get features. The training process with the Haar method uses positive image data as objects and negative images as background. Haar training data that have been obtained, then used to detect fetal head objects automatically. Detection results are then processed to separate the object from the background image, which is then carried out the segmentation process to obtain the fetal head and fetal femur. Then the segmentation method used is Integral Projection to get the fetal head circumference and Find Contour to get the fetal femur. The parameters used to determine gestational ages are biparietal diameter and femur length. Based on experiments that have been done, obtained an accuracy rate of 97.77% using the proposed method for estimating gestational age automatically. Measurements are obtained by comparing the results of the doctor's diagnosis using manual measurements on an ultrasound machine.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8594 DO - 10.18517/ijaseit.10.2.8594

RefWorks

RT Journal Article
ID 8594
A1 Sigit, Riyanto
A1 Rahayu, Khusnul Danny
A1 Nadiyah, Putri
A1 Yuniarti, Heny
A1 Anwar, -
T1 Automatic Detection of Fetal Head and Fetal Measurement on Birth Time Estimation
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 2
YR 2020
SP 447
OP 454
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 ultrasound; fetal biometry; learning-based; biparietal diameter; femur length; integral projection.
AB 

Fetal biometry in ultrasound (USG) is a routine activity that can be used to determine the gestational age of a baby. Accuracy is needed when measurements are made. However, the low quality of ultrasound images and manual measurement that takes a long time and give rise to many different variations of values from each doctor or sonographer. Thus the measurement results obtained are less accurate. From these problems, the development of automatic detection and measurement of the fetal head is needed. One of them is by using a learning-based system method that will carry out the training process using Haar training to get features. The training process with the Haar method uses positive image data as objects and negative images as background. Haar training data that have been obtained, then used to detect fetal head objects automatically. Detection results are then processed to separate the object from the background image, which is then carried out the segmentation process to obtain the fetal head and fetal femur. Then the segmentation method used is Integral Projection to get the fetal head circumference and Find Contour to get the fetal femur. The parameters used to determine gestational ages are biparietal diameter and femur length. Based on experiments that have been done, obtained an accuracy rate of 97.77% using the proposed method for estimating gestational age automatically. Measurements are obtained by comparing the results of the doctor's diagnosis using manual measurements on an ultrasound machine.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8594 DO - 10.18517/ijaseit.10.2.8594