| Atherosclerosis is a systemic disease of the vessel wall that occurs in the aorta, carotid, coronary and peripheral arteries. Atherosclerotic plaques in coronary arteries may cause the narrowing (stenosis) or complete occlusion of the arteries and lead to serious results such as heart attacks and strokes. Medical imaging techniques such as X-ray angiography and computed tomography angiography (CTA) have greatly assisted the diagnosis of atherosclerosis in living patients. Analyzing and quantifying vessels in these images, however, is an extremely laborious and time consuming task if done manually. A novel image segmentation approach and a quantitative shape analysis approach are proposed to automatically isolate the coronary arteries and measure important parameters along the vessels.; The segmentation method is based on the active contour model using the level set formulation. Regional statistical information is incorporated in the framework through Bayesian pixel classification. A new conformal factor and an adaptive speed term are proposed to counter the problems of contour leakage and narrowed vessels resulting from the conventional geometric active contours. The proposed segmentation framework is tested and evaluated on a large amount of 2D and 3D, including synthetic and real 2D vessels, 2D non-vessel objects, and eighteen 3D clinical CTA datasets of coronary arteries.; The centerlines of the vessels are proposed to be extracted using harmonic skeletonization technique based on the level contour sets of the harmonic function, which is the solution of the Laplace equation on the triangulated surface of the segmented vessels. The cross-sectional areas along the vessels can be measured while the center-line is being extracted. Local cross-sectional areas can be used as a direct indicator of stenosis for diagnosis. A comprehensive validation is performed by using digital phantoms and real CTA datasets.; This study provides the possibility of fully automatic analysis of coronary atherosclerosis from CTA images, and has the potential to be used in a real clinical setting along with a friendly user interface. Comparing to the manual segmentation which takes approximately an hour for a single dataset, the automatic approach on average takes less than five minutes to complete, and gives more consistent results across datasets. |
