Common Carotid Artery Intima Media Thickness Segmentation Survey

Authors: L. Ashok Kumar, C. Nagarajan

Abstract:

The ultrasound imaging is very popular to diagnosis the disease because of its non-invasive nature. The ultrasound imaging slowly produces low quality images due to the presence of spackle noise and wave interferences. There are several algorithms to be proposed for the segmentation of ultrasound carotid artery images but it requires a certain limit of user interaction. The pixel in an image is highly correlated so the spatial information of surrounding pixels may be considered in the process of image segmentation which improves the results further. When data is highly correlated, one pixel may belong to more than one cluster with different degree of membership. There is an important step to computerize the evaluation of arterial disease severity using segmentation of carotid artery lumen in 2D and 3D ultrasonography and in finding vulnerable atherosclerotic plaques susceptible to rupture which can cause stroke.

Keywords: IMT measurement, Image Segmentation, common carotid artery, internal and external carotid arteries, ultrasound imaging.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099802

References:

[1] D. Cheng, A. Schmidt, K. Cheng, and H. Burkhardt, “Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images,” Comput. Methods Progr. Biomed., vol. 67, no. 1, pp. 27–37, 2002.
[2] Q. Liang, I. Wendelhag, J. Wikstrand, and T. Gustavsson, “A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images,” IEEE Trans.Med. Imag., vol. 19, no. 2, pp. 127–142, Feb. 2000.
[3] A. Mojsilovic, M. Popovic, N. Amodaj, R. Babic, and M. Ostojic, “Automatic segmentation of intravascular ultrasound images: A texturebased approach,” Ann. Biomed. Eng., vol. 25, pp. 1059–1071, 1997.
[4] B. Sumengen and B. Manjunath, “Multi-scale edge detection and image segmentation,” in Proc. European Signal Processing Conference, 2005.
[5] S. S. Al-amri and N. V. Kalyankar, “Image segmentation by using threshold techniques,” Journal of Computing, vol. 2, no. 5, May 2010.
[6] H. G. Kaganami and Z. Beij, “Region based detection versus edge detection,” IEEE Transactions on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1217-1221, 2009.
[7] M. Sarif, M. Raza, and S. Mohsin, “Face recognition using edge information and DCT,” Sindh Univ. Res. Jour. (Sci. Ser.), vol. 43, no. 2, pp. 209-214, 2011.
[8] S. Naz, H. Majeed, and H. Irshad, “Image segmentation using fuzzy clustering: A survey,” in Proc. 6th International Conference on Emerging Technologies, 2010, pp. 181-186.