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A Method for Segmentation of Carotid Artery Wall from Three-dimensional Ultrasound Images

Author: LiHe
Tutor: HouWenGuang
School: Huazhong University of Science and Technology
Course: Biomedical Engineering
Keywords: Image segmentation carotid atherosclerosis MAB LIB tracking
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Type: Master's thesis
Year: 2013
Downloads: 6
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Abstract


Atherosclerosis is a main underlying cause of vascular diseases, leading to manydeaths around the globe. Carotid artery is particularly vulnerable to atherosclerosis,especially at the location of bifurcation (BF), makes it a hotspot in recent research works.Carotid atherosclerosis starts with the thickening of the artery wall, and followed by thegeneration of plaques; the accumulation of plaques then leads to narrowing of carotidartery, resulting in embolism, a dislodgement of plaque fragments which travel to the brainand causes most strokes. Therefore, it is of great value to detect atherosclerosis early toprevent a deadly scenario.Medical image processing, including image segmentation, is an indispensable tool forearly detection of atherosclerosis. In particular, automated or semi-automatedsegmentation of the carotid artery from B-mode ultrasound (US) images is highly desiredfor monitoring atherosclerosis progression and regression. However, it is a verychallenging task, since the image speckle, artifacts and weak boundaries in US imagesmay lead segmentation failure. A robust ultrasound image segmentation technique musttake these properties into account.To address the above problems, this thesis presents a novel method used to segmentthe media-adventitia boundary (MAB) and the lumen-intima boundary (LIB) of the carotidartery from three-dimensional ultrasound images. A segmentation-tracking-segmentationpattern with a level set method for segmentation and a tissue similarity based method fortracking is adopted. The level set method incorporates information from different sourcesand combines these information based energies into a hybrid energy, the evolvingcomputing of which leads to an accurate segmentation result on each slice. The trackingmethod makes it convenient to start the segmentation process on current slice once thetarget boundaries on previous slice are segmented.The performance of the method in this thesis is tested on a number of available2Dimages (which are generated from slicing3D images), and encouraging results areobtained. Statistical test on these results also shows no significant difference existsbetween our method and manual method (the ground truth).

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