Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Extraction of Blood Flow Area in Brachial Artery for Suspicious Hypertension Patients from Color Doppler Sonography with Fuzzy C-Means Clustering

  • Kim, Kwang Baek (Division of Computer Software Engineering, Silla University) ;
  • Song, Doo Heon (Department of Computer Games, Yong-In Songdam College) ;
  • Yun, Sang-Seok (Division of Mechanical Convergence Engineering, Silla University)
  • Received : 2018.10.05
  • Accepted : 2018.12.06
  • Published : 2018.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Color Doppler sonography is a useful tool for examining blood flow and related indices. However, it should be done by well-trained operator, that is, operator subjectivity exists. In this paper, we propose an automatic blood flow area extraction method from brachial artery that would be an essential building block of computer aided color Doppler analyzer. Specifically, our concern is to examine hypertension suspicious (prehypertension) patients who might develop their symptoms to established hypertension in the future. The proposed method uses fuzzy C-means clustering as quantization engine with careful seeding of the number of clusters from histogram analysis. The experiment verifies that the proposed method is feasible in that the successful extraction rates are 96% (successful in 48 out of 50 test cases) and demonstrated better performance than K-means based method in specificity and sensitivity analysis but the proposed method should be further refined as the retrospective analysis pointed out.

Keywords

E1ICAW_2018_v16n4_258_f0001.png 이미지

Fig. 1. Preprocessing steps: (a) input sonography, (b) binarized, (c) focused on ROI, and (d) after noise removal.

E1ICAW_2018_v16n4_258_f0002.png 이미지

Fig. 2. Preparation for FCM based quantization. (a) Picks from histogram analysis and (b) Typical membership function for FCM quantization.

E1ICAW_2018_v16n4_258_f0003.png 이미지

Fig. 3. Effect of FCM quantization. (a) Quantized by FCM and (b) Extracted by human (red).

E1ICAW_2018_v16n4_258_f0004.png 이미지

Fig. 4. Examples of successful extractions. (a) Input and (b) Successful extraction.

E1ICAW_2018_v16n4_258_f0005.png 이미지

Fig. 5. Different blood flow area extractions by FCM and K-means: (a) Original input, (b) Extraction by FCM, and (c) Extraction by K-means.

E1ICAW_2018_v16n4_258_f0006.png 이미지

Fig. 6. Failed extractions. (a) Case 1 and (b) Case 2.

References

  1. S. G. Sheps, "Doppler ultrasound: What is it used for?" 2016 [Internet], Available: https://www.mayoclinic.org/doppler-ultrasound/expert-answers/faq-20058452.
  2. M. Kelm, "Flow-mediated dilatation in human circulation: diagnostic and therapeutic aspects," American Journal of Physiology-Heart and Circulatory Physiology, vol. 282, no. 1, pp. H1-H5, 2002. DOI: 10.1152/ajpheart.2002.282.1.H1.
  3. R. A. Harris, S. K. Nishiyama, D. W. Wray, and R. S. Richardson, "Ultrasound assessment of flow-mediated dilation," Hypertension, vol. 55, no. 5, pp. 1075-1085, 2010. DOI: 10.1161/HYPERTENSIONAHA.110.150821.
  4. D. H. Thijssen, M. A. Black, K. E. Pyke, J. Padilla, G. Atkinson, R. A. Harris, et al., "Assessment of flow-mediated dilation in humans: a methodological and physiological guideline," American Journal of Physiology-Heart and Circulatory Physiology, vol. 300, no. 1, pp. H2-H12, 2011. DOI: 10.1152/ajpheart.00471.2010.
  5. W. S. Calderon-Gerstein, A. Lopez-Pena, R. Macha-Ramirez, A. Bruno-Huaman, R. Espejo-Ramos, S. Vilchez-Bravo, M. Ramirez-Brena, M. Damian-Mucha, and A. Matos-Mucha, "Endothelial dysfunction assessment by flow-mediated dilation in a high-altitude population," Vascular Health and Risk Management, vol. 13, pp. 421-426, 2017. DOI: 10.2147/VHRM.S151886.
  6. A. J. Flammer, T. Anderson, D. S. Celermajer, M. A. Creager, J. Deanfield, P. Ganz, et al., "The assessment of endothelial function: from research into clinical practice," Circulation, vol. 126, no. 6, pp. 753-767, 2012. DOI: 10.1161/CIRCULATIONAHA.112.093245.
  7. B. S. Lee, K. A. Kim, and M. G. Lee, "Comparison of physical fitness, blood lipid, inflammation marker, and cardiovascular function between normal and impaired fasting glucose in elderly women," The Korean Journal of Physical Education, vol. 55, no. 6, pp. 715-725, 2016.
  8. J. K. Kim, K. A. Kim, H. M. Choi, S. K. Park, and C. L. Stebbins, "Grape seed extract supplementation attenuates the blood pressure response to exercise in prehypertensive men," Journal of Medicinal Food, vol. 21, no. 5, pp. 445-453, 2018. DOI: 10.1089/jmf.2017.0133.
  9. A. L. Mohamed, J. Yong, J. Masiyati, L. Lim, and S. C. Tee, "The prevalence of diastolic dysfunction in patients with hypertension referred for echocardiographic assessment of left ventricular function," The Malaysian Journal of Medical Sciences, vol. 11, no. 1, pp. 66-74, 2004.
  10. A. V. Chobanian, G. L. Bakris, H. R. Black, W. C. Cushman, L. A. Green, J. L. Izzo Jr, et al., "Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure," Hypertension, vol. 42, no. 6, pp. 1206-1252, 2003. DOI: 10.1161/01.hyp.0000107251.49515.c2.
  11. R. Rossi, E. Chiurlia, A. Nuzzo, E. Cioni, G. Origliani, and M. G. Modena, "Flow-mediated vasodilation and the risk of developing hypertension in healthy postmenopausal women," Journal of the American College of Cardiology, vol. 44, no. 8, pp. 1636-1640, 2004. DOI: 10.1016/j.jacc.2004.07.027.
  12. M. C. Corretti, T. J. Anderson, E. J. Benjamin, D. Celermajer, F. Charbonneau, M. A. Creager, et al., "Guidelines for the ultrasound assessment of endothelial-dependent flow-mediated vasodilation of the brachial artery: a report of the International Brachial Artery Reactivity Task Force," Journal of the American College of Cardiology. vol. 39, no. 2, pp. 257-265, 2002. DOI: 10.1016/S0735-1097(01)01746-6.
  13. T. J. DuBose and A. L. Baker, "Confusion and direction in diagnostic Doppler sonography," Journal of Diagnostic Medical Sonography, vol. 25, no. 3, pp. 173-177, 2009. DOI: 10.1177/8756479309335681.
  14. S. Rosati, G. Balestra, F. Molinari, U. R. Acharya, and J. S. Suri, "A selection and reduction approach for the optimization of ultrasound carotid artery images segmentation," in Machine Learning in Healthcare Informatics. Heidelberg: Springer, pp. 309-332, 2014. DOI: 10.1007/978-3-642-40017-9_14.
  15. J. Park, D. H. Song, H. Nho, H. M. Choi, K. A. Kim, H. J. Park, and K. B. Kim, "Automatic segmentation of brachial artery based on fuzzy C-means pixel clustering from ultrasound images," International Journal of Electrical and Computer Engineering, vol. 8, no. 2, pp. 638-643, 2018. DOI: 10.11591/ijece.v8i2.pp638-643.
  16. K. B. Kim, D. H. Song, and H. J. Park, "Automatic extraction of appendix from ultrasonography with self-organizing map and shape-brightness pattern learning," BioMed Research International, vol. 2016, article ID. 5206268, 2016. DOI: 10.1155/2016/5206268.
  17. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ: Prentice-Hall, 2007.
  18. S. Lloyd, "Least squares quantization in PCM," IEEE Transactions on Information Theory, vol. 28 no. 2, pp 129-137, 1982. DOI: 10.1109/TIT.1982.1056489.
  19. S. I. Park, H. J. Park, and K. B. Kim, "Appendix analysis from ultrasonography with cubic spline interpolation and K-means clustering," International Journal of Bio-Science and Bio-Technology, vol. 7, no. 1, pp. 1-10, 2015. DOI: 10.14257/ijbsbt.2015.7.1.01.
  20. A. K. Panda, M. Kumar, M. K. Chaudhary, and A. A. K. Gupta, "Brain tumour extraction from MRI images using K-means clustering," Brain, vol. 4 no. 4, pp 356-359, 2016. DOI: 10.17148/IJIREEICE.2016.4490.
  21. H. J. Lee, D. H. Song, and K. B. Kim, "Effective computer-assisted automatic cervical vertebrae extraction with rehabilitative ultrasound imaging by using K-means clustering," International Journal of Electrical and Computer Engineering, vol. 6, no. 6, pp 2810-2817, 2016. DOI: 10.11591/ijece.v6i6.pp2810-2817.

Cited by

  1. Diagnosis of Pet by Using FCM Clustering vol.26, pp.2, 2018, https://doi.org/10.9708/jksci.2021.26.02.039