Projects:BloodVesselSegmentation

Back to Georgia Tech Algorithms

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 a local region based energy and the evolution based on that energy that was used to detect the soft plaque.

Local Region based energy

For the purpose of soft plaque detection we used a hybrid energy scheme proposed by Lankton et al. This scheme blends the benefits of the geodesic active contours and the region based active contours and is accomplished by forming a geodesic energy from local regions around the curve (zero level set). The resulting flow is more robust to initial curve placement and image noise, like the region-based flows, but also capable of finding significant local minima and partitioning the image without making global assumptions about its makeup. This makes it ideal for soft plaque detection.

Key Investigators

• Georgia Tech Algorithms:Yan Yang, Allen Tannenbaum

Publications

In Print

• NA-MIC Publications Database