Difference between revisions of "Projects:TissueClassificationWithNeighborhoodStatistics"
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| + | Back to [[Algorithm:Utah|Utah Algorithms]] | ||
| + | __NOTOC__ | ||
= Tissue Classification with Neighborhood Statistics = | = Tissue Classification with Neighborhood Statistics = | ||
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We have implemented the MRI Tissue Classification Algorithm described in the reference below. Classes for non-parametric density estimation and automatic parameter selection have been implemented as the basic framework on which we build the classification algorithm. | We have implemented the MRI Tissue Classification Algorithm described in the reference below. Classes for non-parametric density estimation and automatic parameter selection have been implemented as the basic framework on which we build the classification algorithm. | ||
| − | + | = Description = | |
* The stochastic non-parametric density estimation framework is very general and allows the user to change kernel types (we have coded isotropic Gaussian, but additional kernels can easily be derived from the same parent class) and sampler types (for example local vs. global image sampling as well as sampling in non-image data) as template parameters. | * The stochastic non-parametric density estimation framework is very general and allows the user to change kernel types (we have coded isotropic Gaussian, but additional kernels can easily be derived from the same parent class) and sampler types (for example local vs. global image sampling as well as sampling in non-image data) as template parameters. | ||
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* Currently, we are registering atlas images to our data using the stand-alone LandmarkInitializedMutualInformationRegistration application. Ideally, we'd like to incorporate an exiting registration algorithm into our code so that classification can be carried out in one step. The initialization to the registration can be provided as command line arguments. | * Currently, we are registering atlas images to our data using the stand-alone LandmarkInitializedMutualInformationRegistration application. Ideally, we'd like to incorporate an exiting registration algorithm into our code so that classification can be carried out in one step. The initialization to the registration can be provided as command line arguments. | ||
| − | + | = Key Investigators = | |
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| + | Utah: Tolga Tasdizen, Suyash Awate, Ross Whitaker | ||
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| + | = Publications = | ||
| − | * | + | ''In Print'' |
| + | * [http://www.na-mic.org/publications/pages/display?search=TissueClassificationWithNeighborhoodStatistics&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Tissue Classification with Neighborhood Statistics] | ||
| − | + | [[Category:MRI]] [[Category: Statistics]] [[Category:Registration]] | |
Latest revision as of 20:29, 11 May 2010
Home < Projects:TissueClassificationWithNeighborhoodStatisticsBack to Utah Algorithms
Tissue Classification with Neighborhood Statistics
We have implemented the MRI Tissue Classification Algorithm described in the reference below. Classes for non-parametric density estimation and automatic parameter selection have been implemented as the basic framework on which we build the classification algorithm.
Description
- The stochastic non-parametric density estimation framework is very general and allows the user to change kernel types (we have coded isotropic Gaussian, but additional kernels can easily be derived from the same parent class) and sampler types (for example local vs. global image sampling as well as sampling in non-image data) as template parameters.
- The classification class uses the stochastic non-parametric density estimation framework to implement the algorithm in the reference below.
- An existing ITK bias correction method has been incorporated into the method.
- Currently, we are registering atlas images to our data using the stand-alone LandmarkInitializedMutualInformationRegistration application. Ideally, we'd like to incorporate an exiting registration algorithm into our code so that classification can be carried out in one step. The initialization to the registration can be provided as command line arguments.
Key Investigators
Utah: Tolga Tasdizen, Suyash Awate, Ross Whitaker
Publications
In Print
