Projects:BloodVesselSegmentation

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Blood Vessel Segmentation

Atherosclerosis is a systematic disease of the vessel wall that occurs in the aorta, carotid, coronary and peripheral arteries. Atherosclerotic plaques in coronary arteries may cause stenosis (narrowing) or complete occlusion of the arteries and lead to serious results such as heart attacks. Imaging techniques have greatly assisted the diagnoses and treatment procedures of atherosclerosis. Three dimensional imaging such as CTA for coronary arteries is a relatively new approach but has great potentials for detecting and evaluating coronary calcification and stenosis. Fig. 1 (b) shows an example of the 3D reconstruction of coronary arteries and the aorta.

Description

A novel image segmentation approach is proposed combining Bayesian pixel classification method and the active surface model in a level set formulation to extract coronary arteries from CT angiography images. Fig. (2) shows the reconstructed coronary arteries from three different patients, and Fig. (3) are sample slices showing the original images and the delineated vessels as cross-sections.

Once the surface of the coronaries are reconstructed, further shape analysis and measurements can be conducted based on it. Fig. (4) shows the results of performing centerline extraction using a hamonic skeletonization technique [3]. The skeletons can then serve as a guide for finding the perpendicular planes to the arteries, and these planes are used to intersect with the vessel in order to measure the local cross-sectional areas, as shown in Fig. (5).

Soft Plaque Detection and Segmentation

Recent studies have shown that the soft plaque is more vulnerable to rupture than the hard plaque. Hence it becomes necessary to develop methods to detect and segment the soft plaque automatically. The soft plaque has an intensity that lies between the intensities of the blood lumen and the cardiac muscle, thus making it difficult to be detected using the energy calculated globally. In the following section, we briefly discuss the local region based energy and evolution that we used to detect the soft plaque.

Key Investigators

• Georgia Tech Algorithms:Yan Yang, Allen Tannenbaum

Publications

In Print

• NA-MIC Publications Database