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Detections of Intima-Media Thickness in B-Mode Carotid Artery Images Using Segmentation Methods

  • V. Savithri
  • S. Purushothaman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 216)

Abstract

This study presents the investigations carried out on carotid artery to identify the intima-media thickness of carotid artery that affected with plaques. B-mode ultrasound image video of the artery has been used as the data for processing. The frames of the video are processed to know the plaque properties of the artery. In order to achieve this, two segmentation processing techniques have been used on each frame. The features extracted from the frames are consolidated to know the conditions of the artery. Information of a frame are converted into features. The values of the features are estimated by artificial neural network (ANN) algorithm. ANN has not been used extensively by the past. ANN is used in estimating the plaque thickness in the carotid artery.

Keywords

B-mode Back propagation network Pattern recognition Artificial neural network Clustering 

References

  1. 1.
    Santhiyakumari, N., Madheswaran, M.: Intelligent medical decision system for identifying ultrasound carotid artery images with vascular disease. Int. J. Comput. Appl. 1(13), 32–39 (2010)Google Scholar
  2. 2.
    Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern. Recogn. 40, 825–838 (2007)CrossRefMATHGoogle Scholar
  3. 3.
    Lei, W.K., Li, B.N., Dong, M.C., Vai, M.I.: AFC-ECG: an adaptive fuzzy ECG classifier. In: Proceedings of the 11th World Congress on Soft Computing in Industrial Applications (WSC11). Advances in Soft Computing,Springer Berlin,Heidelberg vol. 39, pp. 189–199 (2007) Google Scholar
  4. 4.
    Markos, G., Tsipouras, Themis, P., Exarchos, Dimitrios, I., Fotiadis, Anna, P., Kotsia, Konstantinos, V., Vakalis, Naka, K.K., Michalis, L.K.: Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Trans. Inform. Technol. Biomed. 12(4), 447–456 (2008)Google Scholar
  5. 5.
    Li, B.N., Chui, C.K., Ong, S.H., Chang, S.: Integrating FCM and level sets for liver tumor segmentation. In: Proceedings of the 13th International Conference on Biomedical Engineering, (ICBME 2008) Singapore, 3–6, December 2008 Google Scholar
  6. 6.
    Li, Bing Nan, Chuti, Chee Kong, Chang, Stephen, Ong, S.H.: Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Comput. Biol. Med. 41(1), 1–10 (2011)CrossRefGoogle Scholar
  7. 7.
    Amartur, S.C., Piraino, D., Takefuji, Y.: Optimization neural networks for the segmentation of magnetic resonance images. IEEE Trans. Med. Imaging 2(2), 215–220 (1992)CrossRefGoogle Scholar
  8. 8.
    Levinski, K., Sourin, A., Zagorodnov, V.: Interactive surface-guided segmentation of brain MRI data. Comput. Biol. Med. 39(12), 1153–1160 (2009)CrossRefGoogle Scholar
  9. 9.
    Paragios, N.: A level set approach for shape-driven segmentation and tracking of left ventricle. IEEE Trans. Med. Imaging 22, 773–776 (2003)CrossRefGoogle Scholar
  10. 10.
    Suri, J.S.: Two-dimensional fast magnetic resonance brain segmentation. IEEE Eng. Med. Biol 20, 84–95 (2001)CrossRefGoogle Scholar
  11. 11.
    Ozbay, Y., Ceylan, M.: Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network. Ultrasound Med. Biol. 37(3), 287–295 (2006)Google Scholar
  12. 12.
    Wendelhag, I., Gustavsson, T., Suurkula, M., Berglund, G., Wikstrand, J.: Ultrasound measurement of wall thickness in the carotid artery: Fundamental principles and description of a computerized analysing system. Clin. Physiol. 11, 565–577 (1991)CrossRefGoogle Scholar
  13. 13.
    Wendelhag, I., Liang, Q., Gustavsson, T., Wikstrand, J.: A new automated computerized analyzing system simplifies readings and reduces the variability in ultrasound measurement of intima-media thickness. Stroke 28, 2195–2200 (1997)CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceMother Teresa Women’s UniversityKodaikanalIndia
  2. 2.PET Engineering CollegeTirunelveliIndia

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