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.
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References
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)
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)
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)
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)
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
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)
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)
Levinski, K., Sourin, A., Zagorodnov, V.: Interactive surface-guided segmentation of brain MRI data. Comput. Biol. Med. 39(12), 1153–1160 (2009)
Paragios, N.: A level set approach for shape-driven segmentation and tracking of left ventricle. IEEE Trans. Med. Imaging 22, 773–776 (2003)
Suri, J.S.: Two-dimensional fast magnetic resonance brain segmentation. IEEE Eng. Med. Biol 20, 84–95 (2001)
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)
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)
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)
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Savithri, V., Purushothaman, S. (2014). Detections of Intima-Media Thickness in B-Mode Carotid Artery Images Using Segmentation Methods. In: Sathiakumar, S., Awasthi, L., Masillamani, M., Sridhar, S. (eds) Proceedings of International Conference on Internet Computing and Information Communications. Advances in Intelligent Systems and Computing, vol 216. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1299-7_44
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DOI: https://doi.org/10.1007/978-81-322-1299-7_44
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