Difference between revisions of "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 = | |
[[Image:Fig1yan.PNG | Figure 1]] | [[Image:Fig1yan.PNG | Figure 1]] | ||
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[[Image:Fig5yan.PNG | Figure 5]] | [[Image:Fig5yan.PNG | Figure 5]] | ||
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| + | ''Soft Plaque Detection and Segmentation'' | ||
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| + | 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. | ||
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| + | ''Vessel segmentation using Tubular Surface Extraction framework'' | ||
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| + | In this work, we are extending the Tubular Surface Extraction framework of Mohan et al towards segmenting vessel structures. The blood vessel is modeled as a tube with a center-line and a radius function associated with each point. Further, the work is also being extended to accomodate evolution of end points. This allows a segmentation framework where a portion of the main branch of the vessel tree can be selected as input and the framework evolves this to capture the entire vessel tree. Fig. 10 shows the results from the application of the fixed end points version of this framework. | ||
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| + | [[Image:Mohan_Tubular_Vessels_1.PNG | Figure 10]] | ||
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| + | = Key Investigators = | ||
| − | + | * Georgia Tech Algorithms:Vandana Mohan, Shawn Lankton, Yan Yang, Ponnappan Arumuganainar, Allen Tannenbaum | |
| − | + | = Publications = | |
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| − | ''' | + | ''In Print'' |
| + | * [http://www.na-mic.org/publications/pages/display?search=BloodVesselSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database on Blood Vessel Segmentation] | ||
| − | + | [[Category: Segmentation]] [[Category:CT]] | |
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Latest revision as of 00:58, 16 November 2013
Home < Projects:BloodVesselSegmentationBack to Stony Brook University 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.
Vessel segmentation using Tubular Surface Extraction framework
In this work, we are extending the Tubular Surface Extraction framework of Mohan et al towards segmenting vessel structures. The blood vessel is modeled as a tube with a center-line and a radius function associated with each point. Further, the work is also being extended to accomodate evolution of end points. This allows a segmentation framework where a portion of the main branch of the vessel tree can be selected as input and the framework evolves this to capture the entire vessel tree. Fig. 10 shows the results from the application of the fixed end points version of this framework.
Key Investigators
- Georgia Tech Algorithms:Vandana Mohan, Shawn Lankton, Yan Yang, Ponnappan Arumuganainar, Allen Tannenbaum
Publications
In Print
