Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic


eISSN: 1178-5608




VOLUME 8 , ISSUE 1 (March 2015) > List of articles


Wisnu Jatmiko * / Ikhsanul Habibie / M. Anwar Ma’sum / Robeth Rahmatullah / I Putu Satwika

Keywords : ultrasound, automated system, fetal organ detection, fetal parameters measurement, telehealth.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 697-719, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 05-November-2014 / Accepted: 29-January-2015 / Published Online: 01-March-2015



One of the most profound use of ultrasound imaging is fetal growth monitoring. Conventionally, physicians will perform manual measurements of several parameters of the ultrasound images to draw some conclusion of the fetal condition by manually annotating the fetal images on the ultrasound device interface. However, performing manual annotation of fetal images will require significant amount of time considering the number of patients an obstetrician can have. In this paper, an integrated automatic system for fetal growth monitoring and detection is proposed. This system will be able to automatically measuring the parameters of fetal head, abdomen, femur, and humerus. In addition to automated image detection, we also propose an integrated telehealth monitoring system to provide better access of ultrasound monitoring for patients that lives in rural areas. A new approach of fetal image detection is also proposed by using AdaBoost.MH boosting algorithm that is combined with an improved efficient Hough Transform for detecting ellipse-like organs such as head and abdomen. Experiments of the method are tested on real ultrasound image dataset. The detection was applied on 2D ultrasound images to perform fetal object measurement to approximate the Head Circumference (HC) and Biparietal Diameter (BPD), Femur Length (FL), and Humerus Length (HL).

Content not available PDF Share



[1]. N. Koizumi, J. Seo, Y. Suzuki, D. Lee, K. Ota, A. Nomiya,S. Yoshizawa, K. Yoshinaka, N. Sugita, Y. Matsumoto, Y. Homma, andM. Mitsuishi, “A control framework for the non-invasive ultrasoundthe agnostic system,” in Intelligent Robots and Systems, 2009. IROS2009. IEEE/RSJ International Conference on, Oct. 2009, pp. 4511–4516.
[2]. C. Castellini and D. Gonzalez, “Ultrasound imaging as a human machine interface in a realistic scenario,” in Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on, Nov.2013, pp. 1486–1492.
[3]. G. Carneiro, B. Georgescu, S. Good, and D. Comaniciu, “Detectionand measurement of fetal anatomies from ultrasound images usinga constrained probabilistic boosting tree,” Medical Imaging, IEEETransactions on, vol. 27, no. 9, pp. 1342–1355, Sept. 2008.
[4]. L. Gupta, R. Sisodia, V. Pallavi, C. Firtion, and G. Ramachandran, “Segmentation of 2d fetal ultrasound images by exploitingcontext information using conditional random fields,” in Engineeringin Medicine and Biology Society,EMBC, 2011 Annual InternationalConference of the IEEE, Aug. 2011, pp. 7219–7222.
[5]. N.K. Suryadevara and S.C. Mukhopadhyay, “Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly”, IEEE Sensors Journal, Vol. 12, No. 6, June 2012, pp. 1965-1972.
[6]. W. Lu, J. Tan, and R. Floyd, “Automated fetal head detection andmeasurement in ultrasound images by iterative randomized Houghtransform,” Ultrasound Med Biol, vol. 31, no. 7, pp. 929–936, Jul.2005.
[7]. P. Kultanen, L. Xu, and E. Oja, “Randomized houghtransform(rht),” in Pattern Recognition, 1990. Proceedings, 10th International Conference on, vol.1 Jun. 1990, pp. 631–635 vol.1.
[8]. I. Satwika, R. Rahmatullah, I. Habibie, A. Nurhadiyatna, and W. Jatmiko, “Improved efficient ellipse hough transform for fetal head measurement,” in Advanced Computer Science and Information System,2013. Proceedings., IEEE International Conference on,vol., no., pp.375,379, 28-29 Sept. 2013.
[9]. Y. Xie and Q. Ji, “A new efficient ellipse detection method,” in PatternRecognition, 2002. Proceedings., 16th International Conference on,vol. 2, 2002, pp. 957–960 vol.2.
[10]. Ma’sum, M.A., Jatmiko W., Tawakal M.I., and Afif F.A. "Automated Fetal Organ Detection And Approximation in Ultrasound Images using Boosting Classifier and Hough Transform."Advanced Computer Science and Information Systems (ICACSIS), 2014 International Conference on,vol., no., pp.455-461, 18-19 Oct. 2014
[11]. Rahmatullah R., Ma’sum, M. A., Aprinaldi1, Mursanto P., and Wiweko B. "Automatic Fetal Organs Segmentation Using Multilayer Super Pixel and Image Moment Feature." Advanced Computer Science and Information Systems (ICACSIS), 2014 International Conference on ,vol., no., pp.415-421, 18-19 Oct. 2014
[12]. Satwika, I.P., Habibie, I., Ma’sum, M.A., Febrian, A., and Budianto, E. "Particle Swarm Optimization based 2-Dimensional Randomized Hough Transform for Fetal Head Biometry Detection and Approximation in Ultrasound Imaging." Advanced Computer Science and Information Systems (ICACSIS), 2014 International Conference on ,vol., no., pp.463-468, 18-19 Oct. 2014
[13]. Isa, Sani Muhamad, et al. "Performance Analysis of ECG Signal Compression using SPIHT." International Journal On Smart Sensing And Intelligent Systems 6.5 (2013): 2011-2039.
[14]. Imah, EllyMatul, Wisnu Jatmiko, and T. Basaruddin. "Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification."International Journal on Smart Sensing and Intelligent Systems 6.5 (2013) : 1891-1917
[15]. R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rdEdition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2010.
[16]. N.K.Suryadevara, A. Gaddam, R.K.Rayudu and S.C. Mukhopadhyay, “Wireless Sensors Network based safe Home to care Elderly People: Behaviour Detection”, Sens. Actuators A: Phys. (2012), doi:10.1016/j.sna.2012.03.020, Volume 186, 2012, pp. 277 – 283.
[17]. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in NeuralNetworks, 1995. Proceedings., IEEE International Conference on,vol. 4, Nov 1995, pp. 1942–1948 vol.4.
[18]. H. D. Cheng, Y. Guo, and Y. Zhang, “A novel hough transform basedon eliminating particle swarm optimization and its applications,”PatternRecogn., vol. 42, no. 9, pp. 1959–1969, Sep. 2009. [Online].Available:
[19]. W. Lu and J. Tan, “Detection of incomplete ellipse in imageswith strong noise by iterative randomized hough transform (irht),”Pattern Recogn., vol. 41, no. 4, pp. 1268–1279, Apr. 2008. [Online].Available:
[20]. R. L. Deter, R. B. Harrist, F. P. Hadlock, and R. J. Carpenter, “Fetal head and abdominal circumferences: I. evaluation of measurement errors,” Journal of Clinical Ultrasound, vol. 10, no. 8, pp. 357–363, 1982. [Online].
[21]. V. Chalana, T. C. Winter, D. R. Cyr, D. R. Haynor, and Y. Kim,“Automatic fetal head measurements from sonographic images,” AcadRadiol, vol. 3, no. 8, pp. 628–635, Aug. 1996.
[22]. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518. December 2001.
[23]. Schapire, R. E., & Singer, Y.,“Improved boosting algorithms using confidence-rated predictions.” Machine Learning,37, 297–336.1999.
[24]. N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.
[25]. Benbouzid, D., Busa-Fekete, R., Casagrande, N., Collin, F. D., &Kégl, B. "MultiBoost: a multi-purpose boosting package". The Journal of Machine Learning Research, 13, pp 549-553. 2012.
[26]. J. Zhang, H. Tang, D. Chen, and Q. Zhang, “destress: Mobile and remote stress monitoring, alleviation, and management platform,” in Global Communications Conference (GLOBECOM), IEEE, 2012, pp. 2036–2041.