Difference between revisions of "2009 Summer Project Week WML SEgmentation"
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| − | Since winter project week in Utah, we have developed/implemented a WML segmentation algorithm using ITK classes. Subtasks implemented include: (1) a skull stripping algorithm working on T1 weighted images; (2) a fuzzy clustering algorithm for tissue segmentation; (3) a parametric model for gain field correction. All these subtasks are implemented by using ITK. The training step uses AdaBoost and the segmenation step uses a support vector machine. | + | Since winter project week in Utah, we have developed/implemented a WML segmentation algorithm using ITK classes. Subtasks implemented include: (1) a skull stripping algorithm working on T1 weighted images; (2) a fuzzy clustering algorithm for tissue segmentation; (3) a parametric model for gain field correction. All of these subtasks are implemented by using ITK. The training step uses AdaBoost and the segmenation step uses a support vector machine. |
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Revision as of 10:38, 20 July 2009
Home < 2009 Summer Project Week WML SEgmentation
Pipeline of WML segmentation
One training dataset (T1, T2, PD, FLAIR images and wml segmentation)
One testing dataset and segmentation result
Key Investigators
- UNC: Minjeong Kim, Dinggang Shen
- GE : Xiaodong Tao, Jim Miller
Objective
We will continue developing and testing the white matter lesion segmentation algorithm implemented using ITK. The goal is to have an initial version ready by the end of the week that can be distributed within NA-MIC community for more extensive testing.
Approach, Plan
We will develop a Slicer module for the white matter lesion segmentation algorithm. Base line results and test will be generated.
Progress
Since winter project week in Utah, we have developed/implemented a WML segmentation algorithm using ITK classes. Subtasks implemented include: (1) a skull stripping algorithm working on T1 weighted images; (2) a fuzzy clustering algorithm for tissue segmentation; (3) a parametric model for gain field correction. All of these subtasks are implemented by using ITK. The training step uses AdaBoost and the segmenation step uses a support vector machine.
References
- Zhiqiang Lao, Dinggang Shen, Dengfeng Liu, Abbas F Jawad, Elias R Melhem, Lenore J Launer, Nick R Bryan, Christos Davatzikos, Computer-Assisted Segmentation of White Matter Lesions in 3D MR images, Using Pattern Recognition, Academic Radiology, 15(3):300-313, March 2008.
