Difference between revisions of "Projects:ProstateSegmentation"
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Two ways are employed to attack the problem. The first way is a combination of Cellular Automata(CA also called Grow Cut) with Geodesic Active Contour(GAC) methods. While the second is using a ellipsoid to match the prostate in 3D image. The details are given below. | Two ways are employed to attack the problem. The first way is a combination of Cellular Automata(CA also called Grow Cut) with Geodesic Active Contour(GAC) methods. While the second is using a ellipsoid to match the prostate in 3D image. The details are given below. | ||
| − | 1. | + | 1. Cellular Automata and Geodesic Active Contour. |
| − | 2. Prostate is usually modeled as an ellipsoid. | + | 1.1 Image segmentation using CA algorithm was proposed by V Vezhnevets and V Konouchine, in "Grow-Cut" - Interactive Multi-Label N-D Image Segmentation. Graphicon, 2005. The algorithm starts from an initialization where two patches in foreground and background, respectively, are picked by hand. Then algorithm iteratively determine the category of each pixel/voxel in the image. |
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| + | 1.2 CA algorithm does not deal with smoothness directly. A GAC step is employed, for one purpose, to smooth the result given by CA and for the other to fine tune the contour. | ||
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| + | 1.3 Both algorithms are implemented in 3D. A ITK-Cellular Automata filter, handling N-D data, has already been completed and submitted into the NA-MIC SandBox. | ||
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| + | 2. Prostate is usually modeled as an ellipsoid. Also, with no control of the global shape, the algorithm is highly influenced by noise and incomplete image information like weak boundary. A shape prior would be used to deal with such situations. However, firstly the prior is learned from a sufficient number of training data, which may not be available. Secondly the shapes need to be aligned in order to be learned or applied in segmentation. However for prostate which is mostly an ellipsoid in shape, there's not much prominent shape feature to drive the alignment and segmentation. | ||
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| + | So we are trying to first model the prostate using an ellipsoid. With that global constrain of shape, extraction it from image would be more robust. Secondly, if the prostate is well captured by an ellipsoid, then starting from there more local method would be used to capture the detail feature of the organ. | ||
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| + | This way of attacking is under developing and some result would be available before the Core 1 meeting in late May. | ||
= Key Investigators = | = Key Investigators = | ||
Revision as of 02:45, 28 April 2008
Home < Projects:ProstateSegmentationBack to Georgia Tech Algorithms
Prostate Segmentation
The objective is to extract the prostate from a 3D ultrasound data set.
Description
Two ways are employed to attack the problem. The first way is a combination of Cellular Automata(CA also called Grow Cut) with Geodesic Active Contour(GAC) methods. While the second is using a ellipsoid to match the prostate in 3D image. The details are given below.
1. Cellular Automata and Geodesic Active Contour.
1.1 Image segmentation using CA algorithm was proposed by V Vezhnevets and V Konouchine, in "Grow-Cut" - Interactive Multi-Label N-D Image Segmentation. Graphicon, 2005. The algorithm starts from an initialization where two patches in foreground and background, respectively, are picked by hand. Then algorithm iteratively determine the category of each pixel/voxel in the image.
1.2 CA algorithm does not deal with smoothness directly. A GAC step is employed, for one purpose, to smooth the result given by CA and for the other to fine tune the contour.
1.3 Both algorithms are implemented in 3D. A ITK-Cellular Automata filter, handling N-D data, has already been completed and submitted into the NA-MIC SandBox.
2. Prostate is usually modeled as an ellipsoid. Also, with no control of the global shape, the algorithm is highly influenced by noise and incomplete image information like weak boundary. A shape prior would be used to deal with such situations. However, firstly the prior is learned from a sufficient number of training data, which may not be available. Secondly the shapes need to be aligned in order to be learned or applied in segmentation. However for prostate which is mostly an ellipsoid in shape, there's not much prominent shape feature to drive the alignment and segmentation.
So we are trying to first model the prostate using an ellipsoid. With that global constrain of shape, extraction it from image would be more robust. Secondly, if the prostate is well captured by an ellipsoid, then starting from there more local method would be used to capture the detail feature of the organ.
This way of attacking is under developing and some result would be available before the Core 1 meeting in late May.
Key Investigators
- Georgia Tech Algorithms: Yi Gao, Allen Tannenbaum
