Deteksi Arteri Karotis pada Citra Ultrasound B-Mode Berbasis Convolution Neural Network Single Shot Multibox Detector

Carotid Artery Detection in B-Mode Ultrasound Images Based on Convolution Neural Network Single Shot Multibox Detector

*I Made Gede Sunarya -  Department of Informatics, Universitas Pendidikan Ganesha, Indonesia
Tita Karlita -  Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Joko Priambodo -  Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Rika Rokhana -  Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Eko Mulyanto Yuniarno -  Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Tri Arief Sardjono -  Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Ismoyo Sunu -  Department of Cardiology and Vascular Medicine, Universitas Indonesia, Indonesia
I Ketut Eddy Purnama -  Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Indonesia
Received: 25 Feb 2019; Revised: 22 Apr 2019; Accepted: 29 Apr 2019; Published: 30 Apr 2019; Available online: 16 Jul 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Article Info
Section: Articles
Language: ID
Statistics: 186 157
Detection of vascular areas (blood vessels) using B-Mode ultrasound images is needed for automatic applications such as registration and navigation in medical operations. This study developed the detection of the carotid artery area using Convolution Neural Network Single Shot Network Multibox Detector (SSD) to determine the bounding box ROI of the carotid artery area in B-mode ultrasound images. The data used are B-Mode ultrasound images on the neck that contain the carotid artery area (primary data). SSD method result is 95% of accuracy which is higher than the Hough transformation method, Ellipse method, and Faster RCNN in detecting carotid artery area in the B-Mode ultrasound image. The use of image enhancement with Gaussian filter, histogram equalization, and Median filters in this method can increase detection accuracy. The best process time of the proposed method is 2.09 seconds so that it can be applied in a real-time system.
object detection; carotid artery; ultrasound B-Mode; convolutional neural network; single shot multibox detector

Article Metrics:

  1. M. Naghavi et al., “From Vulnerable Plaque to Vulnerable Patient: A Call for New Definitions and Risk Assessment Strategies: Part I,” Circulation, vol. 108, no. 15, pp. 1772-1778, 2003.
  2. L. G. Spagnoli et al., “Extracranial Thrombotically Active Carotid Plaque as a Risk Factor for Ischemic Stroke,” JAMA, vol. 292, no. 15, pp. 1845-1852, 2004.
  3. A. J. Lusis, “Atherosclerosis,” Nature, vol. 407, no. 6801, pp. 233-241, 2000.
  4. R. Hameeteman et al., “Carotid Lumen Segmentation and Stenosis Grading Challenge,” in Proceedings of the Carotid Lumen Segmentation and Stenosis Grading Challenge, London, UK, Sept. 2009, pp. 1-16.
  5. H. Tang et al., “Semiautomatic Carotid Lumen Segmentation for Quantification of Lumen Geometry in Multispectral MRI,” Medical Image Analysis, vol. 16, no. 6, pp. 1202-1215, 2012.
  6. F. Mao, J. Gill, D. Downey, and A. Fenster, “Segmentation of Carotid Artery in Ultrasound Images: Method Development and Evaluation Technique,” Medical Physics, vol. 27, no. 8, pp. 1961-1970, 2000.
  7. C. P. Loizou, C. S. Pattichis, A. N. Nicolaides, and M. Pantziaris, “Manual and Automated Media and Intima Thickness Measurements of the Common Carotid Artery,” IEEE Transactions on Ultrasonic Ferroelectrics, and Frequency Control, vol. 56, no. 5, pp. 983-994, 2009.
  8. F. Destrempes, J. Meunier, M. F. Giroux, G. Soulez, and G. Cloutier, “Segmentation in Ultrasonic B-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization,” IEEE Transactions on Medical Imaging, vol. 28, no. 2, pp. 215-229, 2009.
  9. S. Delsanto, F. Molinari, P. Giustetto, W. Liboni, S. Badalamenti, and J. S. Suri, “Characterization of a Completely User-Independent Algorithm for Carotid Artery Segmentation in 2-D Ultrasound Images,” IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 4, pp. 1265-1274, 2007.
  10. X. Yang, M. Ding, L. Lou, M. Yuchi, W. Qiu, and Y. Sun, “Common Carotid Artery Lumen Segmentation in B-mode Ultrasound Transverse View Images,” International Journal of Image, Graphics and Signal Processing, vol. 3, no. 5, pp. 15, 2011.
  11. G. L. Kate et al., “Noninvasive Imaging of the Vulnerable Atherosclerotic Plaque,” Current Problem in Cardiology, vol. 35, no. 11, pp. 556-591, 2010.
  12. I. M. G. Sunarya, E. Y. Mulyanto, M. H. Purnomo, T. A. Sardjono, I. Sunu, and I. K. E. Purnama, “Carotid Artery B-Mode Ultrasound Image Segmentation based on Morphology , Geometry and Gradient Direction,” in Second International Workshop on Pattern Recognition, Singapore, Jun. 2017, pp. 5-9.
  13. D. D. B. Carvalho et al., “Lumen Segmentation and Motion Estimation in B-Mode and Contrast-Enhanced Ultrasound Images of the Carotid Artery in Patients With Atherosclerotic Plaque,” IEEE Transactions on Medical Imaging, vol. 34, no. 4, pp. 983-993, 2015.
  14. E. Yeom, K. H. Nam, C. Jin, D. G. Paeng, and S. J. Lee, “3D Reconstruction of a Carotid Bifurcation from 2D Transversal Ultrasound Images,” Ultrasonics, vol. 54, no. 8, pp. 2184-2192, 2014.
  15. P. Poudel, C. Hansen, J. Sprung, and M. Friebe, “3D Segmentation of Thyroid Ultrasound Images using Active Contours,” Current Directions in Biomedical Engineering, vol. 2, no. 1, pp. 3-4, 2016.
  16. D. Mozaffarian et al., “AHA Statistical Update Heart Disease and Stroke Statistics - 2015 Update A Report From the American Heart Association,” Circulation, vol. 131, no. 4, pp. e29-e322, 2015.
  17. J. R. H. Kumar, “Automatic Detection of Common Carotid Artery in Transverse Mode Ultrasound Images,” in 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, USA, 2016, pp. 389-393.
  18. S. Golemati, J. Stoitsis, E. G. Sifakis, T. Balkizas, and K. S. Nikita, “Using the Hough Transform to Segment Ultrasound Images of Longitudinal and Transverse Sections of the Carotid Artery,” Ultrasound Medical Biology, vol. 33, no. 12, pp. 1918-1932, 2007.
  19. D. Gil, P. Radeva, and J. Saludes, “Segmentation of Artery Wall in Coronary Ivus Images: A Probabilistic Approach,” Computers in Cardiology, vol. 27, pp. 352-355, 2000.
  20. J. Guerrero, S. E. Salcudean, J. A. McEwen, B. A. Masri, and S. Nicolaou, “Real-time Vessel Segmentation and Tracking for Ultrasound Imaging Applications,” IEEE Transactions on Medical Imaging, vol. 26, no. 8, pp. 1079-1090, 2007.
  21. K. S. Manoj K. Karmakar, Edmund Soh, and V. Chee, Atlas of Sonoanatomy for Regional Anesthesia and Pain Medicine. McGraw-Hill Education, 2018.
  22. F. Aziz and A. J Comerota, “Carotid Artery Stenting Technique,” Medscape, 2018.
  23. J. Huang et al., “Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017, pp. 3296-3297.
  24. W. Liu et al., “SSD : Single Shot MultiBox Detector,” arXiv:1512.02325v5 [cs.CV], Dec. 2015.