An Effective Approach to Plaque Characterization in Ultrasound Images of Carotid Atherosclerosis
M. Anthuvan Lydia, Saranathan College of Engineering; V. Ramya ,Saranathan College of Engineering; J. Eindhumathy ,Saranathan College of Engineering
Atherosclerosis, Carotid Ultrasound, Classification, Discrete Wavelet Transform (DWT), Support Vector Machine (SVM)
Stroke is one of the most important causes of death in the world and the leading cause of serious, long-term disability. There is an urgent need for better techniques to diagnose patients at risk of stroke. In order to increase the accuracy of the diagnosis, parameters aiming to identify vulnerable lesions have been studied using 2D B-mode ultrasound (US) imaging. This survey intends to summarize the techniques for improvement of Ultrasonographic image quality, extraction of good features and system for plaque classification from ultrasound images using image processing is described. The proposed system involves A Plaque Characterization method that use Wavelet Transform that favors extraction of important ultrasound structure which is associated with several risk factors for atherosclerosis and to develop a system for asymptomatic and symptomatic classification of the atherosclerotic carotid plaque in ultrasound images of the carotid artery.
-
[1] N. Tsiaparas et al, “Comparison of Multiresolution Features for Texture Classification of Carotid Atherosclerosis from B- Mode Ultrasound”, IEEE Transactions on Information Technology in Biomedicine, Volume: 15, Issue: 1,130 – 137, January2011.
[2] J. Seabra, L. M. P. (MD), F. e Fernandes, and J. M. Sanches, “Ultrasonographic characterization and identification of symptomatic carotid plaques,” in Engineering in Medicine and Biology Society, EMBS 2010. 32th Annual International Conference of the IEEE, September. 2010.
[3] “A Review of Noninvasive Ultrasound Image processing methods in the analysis of Carotid plaque morphology for the assessment of stroke risk” IEEE Transactions on information technology in biomedicine, vol 14, No 4, July 2010.
[4] E. Kyriacou, M. Pattichis, C. S. Pattichis, A. Mavrommatis,C. I. Christodoulou, S. Kakkos, and A. Nicolaides, “Classifcation of atherosclerotic carotid plaques using morphological analysis on ultrasound images,” J. Appl. Intell., vol. 30, no. 1, pp. 3–23, 2009.
[5] J. Stoitsis, N. Tsiaparas, S. Golemati, and K. S. Nikita, “Characterization of carotid atherosclerotic plaques using frequency-based texture analysis and bootstrap,” in Proc. 28th Annu. Int. Conf. IEEE EMBS, New York,2006,
[6] J. Stoitsis, S. Golemati, K. S. Nikita, and A. N. Nicolaides, “Characterization of carotid atherosclerosis based on motion and texture features and clustering using fuzzy c-means,” in Proc. 26th Annu. Int. Conf. IEEE EMBS, San Francisco, CA, 2004, pp. 1407–1410.
[7] C. I. Christodoulou, C. S. Pattichis, M. Pantziaris, and A. Nicolaides, “Texture-based classification of atherosclerotic carotid
Paper ID: GRDJEV02I050130
Published in: Volume : 2, Issue : 5
Publication Date: 2017-05-01
Page(s): 169 - 174
Published in: Volume : 2, Issue : 5
Publication Date: 2017-05-01
Page(s): 169 - 174