Common Carotid Artery Intima Media Thickness Segmentation Survey

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.

• L. Ashok Kumar
• C. Nagarajan

• IMT measurement
• Image Segmentation
• common carotid artery
• internal and external carotid arteries
• ultrasound imaging.

[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.