Communications of the ACS, Vol 2, No 2 (2009)

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Semi-Automatic Snake-Based Segmentation of Carotid Artery Ultrasound Images

Sherif G. Moursi, Mahmoud R. El-Sakka

Abstract


Carotid ultrasound imaging is one of the clinical diagnostic procedures that can be employed to detect plaque buildup at the carotid artery walls. It is an inexpensive and non-invasive procedure that has no known side effects. Yet, the acquired ultrasound images have poor quality and contain a lot of noise. Active contouring segmentation techniques (also known as snakes or deformable model) are characterized by their robustness to both image noise and boundary gaps. Hence, they are suitable to be used to segment noisy poor quality ultrasound images. One of the major issues of active contouring methods is their sensitivity to the initial contour that is provided by the user. Unless it is drawn close enough to the actual contour, it may lead to unsatisfactory results. Thus, most active contour algorithms require considerable user interaction to provide a good initial contour. This paper presents an efficient algorithm for extracting carotid artery lumens in ultrasound images. It starts by utilizing a rule-based scheme to generate an initial contour for the lumen. This contour is refined using a snake scheme, after carefully adjusting its energies. Our algorithm reduces the user interaction, as the user is only required to place a seed point inside the region of interest. It is worth mentioning that our proposed initial contour generation scheme can be easily integrated as an independent module with any active contouring algorithm. Sensitivity, precision rate, and overlap ratio are utilized to assess the performance of the proposed scheme. The results show that the extracted initial and final contours have a good overlap with contours that are manually segmented by an experienced clinician.


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