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Automatic Fetal Organs Detection and Approximation in Ultrasound Image

Open Access
|Mar 2015

References

  1. Statistics Indonesia (Badan Pusat Statistik (BPS)) and Macro In-ternational, Indonesia Demographic and Health Survey 2007.Calverton, Maryland, USA: BPS and Macro International,2008.
  2. Carneiro, G., Georgescu, B., Good, S., Comaniciu, D. “Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree,”Medical Imaging, IEEE Transactions on , vol.27, no.9, pp.1342,1355, Sept. 200810.1109/TMI.2008.92891718753047
  3. Anquez, J., Angelini, E.D., Grange, G., Bloch, I., “Automatic Segmentation of Antenatal 3-D Ultrasound Images,” Biomedical Engineering, IEEE Transactions on , vol.60, no.5, pp.1388,1400, May. 201310.1109/TBME.2012.223740023292786
  4. Gupta, L., Sisodia, R.S., Pallavi, V., Firtion, C., Ramachandran, G., “Segmentation of 2D fetal ultrasound images by exploiting context information using conditional random fields,” Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE , vol., no., pp.7219,7222, Aug. 30 2011-Sept. 3 2011.
  5. Shrimali, V., Anand, R.S., Kumar, V., “Improved segmentation of ultrasound images for fetal biometry, using morphological operators,” Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE , vol., no., pp.459,462, 3-6 Sept. 200910.1109/IEMBS.2009.533447019964738
  6. Tien Dung Nguyen, Sang Hyun Kim, Kim Nam Chul, “Surface Extraction Using SVM-Based Texture Classification for 3D Fetal Ultrasound Imaging,” Communications and Electronics, 2006. ICCE ‘06. First International Conference on , vol.,no., pp.285,290, 10-11 Oct. 2006.10.1109/CCE.2006.350830
  7. Bibin, L., Anquez, J., de la Plata Alcalde, J.P., Boubekeur, T., Angelini, E.D., Bloch, I. “Wholebody pregnant woman modeling by digital geometry processing with detailed uterofetal unit based on medical images,”Biomedical Engineering, IEEE Transactions on , vol.57, no.10, pp.2346,2358, Oct. 201010.1109/TBME.2010.205336720570763
  8. Mylonas, G.P., Giataganas, P., Chaudery, M., Vitiello, V., Darzi, A., Guang-Zhong Yang, “Autonomous eFAST ultrasound scanning by a robotic manipulator using learning from demonstrations,” Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on , vol., no., pp.3251,3256, 3-7 Nov. 201310.1109/IROS.2013.6696818
  9. Caroline N., Krupa, Alexandre, “Improving ultrasound intensity-based visual servoing: Tracking and positioning tasks with 2D and bi-plane probes,” Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on , vol., no., pp.2837,2842, 25-30 Sept. 2011
  10. Ito, K.., Sugano, S., Iwata, H., “Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis,” Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on , vol., no., pp.4131,4136, 7-12 Oct. 2012.
  11. Satwika, I.P., Tawakal, M.I., Imaduddin, Z., Jatmiko, W., “Efficient incomplete ellipse detection based on minor axis for ultrasound fetal head approximation,” Advanced Computer Science and Information Systems (ICACSIS), 2012 International Conference on , vol., no., pp.191,195, 1-2 Dec. 2012
  12. Schapire, R. E., & Singer, Y., “Improved boosting algorithms using confidence-rated predictions,” Machine Learning,37, pp. 297-336. 1999.10.1023/A:1007614523901
  13. Freund, Y., & Schapire, R. E., “A decision-theoretic generalization of on-line learning and an application to boosting”.Journal of Computer and System Sciences,55, pp.119,139. 1997.10.1006/jcss.1997.1504
  14. Fumera, G., Roli, F. “A theoretical and experimental analysis of linear combiners for multiple classifier systems,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.27, no.6, pp.942-956, June 200510.1109/TPAMI.2005.10915943425
  15. . 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. 201410.1109/ICACSIS.2014.7065897
  16. 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. 201410.1109/ICACSIS.2014.7065883
  17. Satwika, I.P., Habibie, I., Ma’sum, M.A., Febrian, A., and Budianto, E. “Particle Swarm Optimation 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 ,pp.463-468, 18-19 Oct. 201410.1109/ICACSIS.2014.7065898
  18. Isa, Sani Muhamad, et al. “Performance Analysis of ECG Signal Compression using SPIHT.” International Journal On Smart Sensing And Intelligent Systems 6.5 (2013): 2011-2039.10.21307/ijssis-2017-624
  19. Imah, Elly Matul, 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) : 1891-191710.21307/ijssis-2017-619
  20. 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.
  21. Ahonen, T., Hadid, A., and Pietikainen, M. Face Recognition with Local Binary Patterns. Computer Vision – ECCV 2004 (2004), 469–481.
  22. R. McLaughlin, “Randomized Hough Transform: Improved Ellipse Detection with Comparition,” Pattern Recognition Letters, vol. 19, no. 3-4, pp. 299-305, 1998.10.1016/S0167-8655(98)00010-5
  23. M. Nixon and A. Aguado, “Feature Extraction & Image Pocessing Second Edition”. London: Elsevier Ltd. 2008.
  24. Bradski, G., & Kaehler, “Learning OpenCV: Computer vision with the OpenCV library”. O’reilly. 2008
  25. Benbouzid, D., Busa-Fekete, R., Casagrande, N., Collin, F. D., & Kégl, B. “MultiBoost: a multipurpose boosting package”. The Journal of Machine Learning Research, 13, pp 549-553. 2012.
  26. N. K. Suryadevara and S. C. Mukhopadhyay, “Determining Wellness Through An Ambient Assisted Living Environment”, IEEE Intelligent Systems, May/June 2014, pp. 30-37.10.1109/MIS.2014.16
  27. Kégl, B., & Busa-Fekete, R. “Boosting products of base classifiers”. In Proceedings of the 26th Annual International Conference on Machine Learning (pp. 497-504). June 200910.1145/1553374.1553439
  28. Xiang, Liu et all . “Research of Improved LVQ Neural Network by Adaboost Algorithm” Journal of Applied Science 13 (14) pp. 2658-2663. 2013.10.3923/jas.2013.2658.2663
Language: English
Page range: 720 - 748
Submitted on: Nov 6, 2014
Accepted on: Jan 30, 2015
Published on: Mar 1, 2015
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2015 M. Anwar Ma’sum, Wisnu Jatmiko, Budi Wiweko, Anom Bowolaksono, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.