java.lang.Object
- gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMObject
- - gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMDeform
  - - gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMDeformPCA

```
public class CAAMDeformPCA
extends CAAMDeform
```
This is the Java modified version of C++ active appearance model API (AAM_API). It is modified with a subset of required functions for automatic MRI prostate segmentation. AAM-API LICENSE - file: license.txt This software is freely available for non-commercial use such as research and education. Please see the full disclaimer below. All publications describing work using this software should cite the reference given below. Copyright (c) 2000-2003 Mikkel B. Stegmann, mbs@imm.dtu.dk IMM, Informatics & Mathematical Modelling DTU, Technical University of Denmark Richard Petersens Plads, Building 321 DK-2800 Lyngby, Denmark http://www.imm.dtu.dk/~aam/ REFERENCES Please use the reference below, when writing articles, reports etc. where the AAM-API has been used. A draft version the article is available from the homepage. I will be happy to receive pre- or reprints of such articles. /Mikkel ------------- M. B. Stegmann, B. K. Ersboll, R. Larsen, "FAME -- A Flexible Appearance Modelling Environment", IEEE Transactions on Medical Imaging, IEEE, 2003 (to appear) ------------- 3RD PART SOFTWARE The software is partly based on the following libraries: - The Microsoft(tm) Vision Software Developers Kit, VisSDK - LAPACK DISCLAIMER This software is provided 'as-is', without any express or implied warranty. In no event will the author be held liable for any damages arising from the use of this software. Permission is granted to anyone to use this software for any non-commercial purpose, and to alter it, subject to the following restrictions: 1. The origin of this software must not be misrepresented; you must not claim that you wrote the original software. 2. Altered source versions must be plainly marked as such, and must not be misrepresented as being the original software. 3. This notice may not be removed or altered from any source distribution. -- No guarantees of performance accompany this software, nor is any responsibility assumed on the part of the author or IMM. This software is provided by Mikkel B. Stegmann and IMM ``as is'' and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall IMM or Mikkel B. Stegmann be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage. $Revision: 1.4 $ $Date: 2003/04/23 14:49:15 $ Performs Principal Component Analysis on a set of data vectors. The PCA basis can then be used for shape deformation.

Author:

Ruida Cheng

Field Summary

Fields
Modifier and Type	Field	Description
`private CDMatrix`	`m_mEigenVectors`
`private CDVector`	`m_vDataMean`
`private CDVector`	`m_vEigenValues`
`private CDVector`	`m_vEigenValuesOrg`
`private java.util.Vector<CDVector>`	`m_vvData`

Fields inherited from class gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMDeform
m_bValid

Constructor Summary

Constructors
Constructor Description

CAAMDeformPCA()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`BackProject(CDVector param, CDVector synth_obs)`	Back projects a set of PCA model parameters into the original space.
`int`	`CalcNParam(double retained_variance)`	Calulates the needed number of parameters to retain 'retained_variance'.
`void`	`ClearDataItems()`	Deletes the data matrix.
`void`	`Deform(CDVector params, CDVector object)`	Projects a set of PC scores to the original space.
`boolean`	`DoPCA()`	Apply PCA analysis
`boolean`	`DoPCA(boolean bWriteCoVarMatrix)`	Performs the principal component analysis on the data items.
`int`	`Dsyev(int m, double[][] a, double[] vals, double[][] vects)`	Call the lapack routine
`void`	`Dump(java.lang.String szPath)`	Debug method that provides a human readable file dump of the object data.
`CDVector`	`EigenValues()`	Returns the eigen values in vector form.
`CDVector`	`EigenValuesOrg()`	Returns the orignal eigen values in vector form.
`CDMatrix`	`EigenVectors()`	Returns the eigen vectors in matrix form.
`void`	`Filter(CDVector obs)`	Projects an observation into the PCA space and back.
`void`	`FromFile(java.io.DataInputStream fh)`	Reads a PCA object from a binary file.
`void`	`InsertDataItem(CDVector v)`	Inserts a data vector
`double`	`MahalanobisDistance(CDVector params)`	Returns the Mahalanobis distance of a set of PCA parameters.
`CDVector`	`MeanDataItem()`	Returns the mean of all sample vectors.
`int`	`NDataItems()`	Returns the number of data items this basis is based on.
`int`	`NParameters()`	Returns the number of principal parameters.
`int`	`NParametersOrg()`	Returns the number of princal parameters before any truncation.
`double`	`ParameterWeight(int i, boolean asPercentage)`	Returns the eigenvalue of the i-th parameter in absolute numbers or as percentage.
`double`	`ParameterWeightOrg(int i, boolean asPercentage)`	Returns the eigenvalue of the i-th parameter in absolute numbers or as percentage.
`void`	`Project(CDVector obs, CDVector param)`	Projects an observation into the PCA space.
`void`	`ShuffleData()`	Shuffle each dimension in all data items over all observations.
`void`	`ToFile(java.io.DataOutputStream fh)`	Writes a PCA object to a binary file.
`void`	`Truncate(int n)`	Truncate eigenvectors and eigenvalues to retain the 'n' largest.
`int`	`TruncateParallel()`	Truncate eigenvectors and eigenvalues using parallel analysis.
`int`	`TruncateVar(double retained_variance)`	Truncate eigenvectors and eigenvalues to retain 'retained_variance'.
`void`	`UseIdentityTransformation()`	Makes the object use an identity basis instead of PCA basis, i.e.
`boolean`	`VisDMatrixSymmetricEigen(CVisDMatrix A, CVisDVector vals, CVisDMatrix vects)`	Perform symmetric eigen analysis.

Methods inherited from class gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMDeform
IsTruncated, IsValid

Methods inherited from class gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMObject
FromFile, ToFile

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - m_mEigenVectors
```
private CDMatrix m_mEigenVectors
```
  - m_vEigenValues
```
private CDVector m_vEigenValues
```
  - m_vEigenValuesOrg
```
private CDVector m_vEigenValuesOrg
```
  - m_vvData
```
private java.util.Vector<CDVector> m_vvData
```
  - m_vDataMean
```
private CDVector m_vDataMean
```
- Constructor Detail
  - CAAMDeformPCA
```
public CAAMDeformPCA()
```
- Method Detail
  - EigenValues
```
public CDVector EigenValues()
```
    Returns the eigen values in vector form.
    
    Returns:
    
    eigen values
  - EigenValuesOrg
```
public final CDVector EigenValuesOrg()
```
    Returns the orignal eigen values in vector form.
    
    Returns:
    
    eigen values original
  - EigenVectors
```
public CDMatrix EigenVectors()
```
    Returns the eigen vectors in matrix form.
    
    Returns:
    
    eigen vectors
  - NParameters
```
public int NParameters()
```
    Returns the number of principal parameters.
    
    Specified by:
    
    NParameters in class CAAMDeform
    
    Returns:
    
    length of eigen values
  - NParametersOrg
```
public int NParametersOrg()
```
    Returns the number of princal parameters before any truncation.
    
    Specified by:
    
    NParametersOrg in class CAAMDeform
    
    Returns:
    
    eigen values original length
  - ClearDataItems
```
public void ClearDataItems()
```
    Deletes the data matrix.
    
    Specified by:
    
    ClearDataItems in class CAAMDeform
  - NDataItems
```
public final int NDataItems()
```
    Returns the number of data items this basis is based on.
    
    Returns:
    
    data matrix size
  - ParameterWeight
```
public double ParameterWeight(int i,
                              boolean asPercentage)
```
    Returns the eigenvalue of the i-th parameter in absolute numbers or as percentage.
    
    Specified by:
    
    ParameterWeight in class CAAMDeform
    
    Parameters:
    
    i - index
    
    asPercentage - boolean flag to indicate % return or abs number
    
    Returns:
    
    ret the eigenvalue.
  - ParameterWeightOrg
```
public double ParameterWeightOrg(int i,
                                 boolean asPercentage)
```
    Returns the eigenvalue of the i-th parameter in absolute numbers or as percentage.
    
    Specified by:
    
    ParameterWeightOrg in class CAAMDeform
    
    Parameters:
    
    i - index
    
    asPercentage - boolean flag to indicate % return or abs number
    
    Returns:
    
    ret the eigenvalue.
  - InsertDataItem
```
public void InsertDataItem(CDVector v)
```
    Inserts a data vector
    
    Parameters:
    
    v - a data vector
  - UseIdentityTransformation
```
public void UseIdentityTransformation()
```
    Makes the object use an identity basis instead of PCA basis, i.e. essentially a by-pass of this object.
  - DoPCA
```
public boolean DoPCA()
```
    Apply PCA analysis
    
    Returns:
    
    success or not.
  - DoPCA
```
public boolean DoPCA(boolean bWriteCoVarMatrix)
```
    Performs the principal component analysis on the data items. Uses the Eckhart-Young theorem if necessary for reduced memory and computational requirements.
    
    Parameters:
    
    bWriteCoVarMatrix - If true the covariance matrix is written to the current dir. NOTE: Only in the case of more data items than dimensions (samples).
    
    Returns:
    
    success or not
  - VisDMatrixSymmetricEigen
```
public boolean VisDMatrixSymmetricEigen(CVisDMatrix A,
                                        CVisDVector vals,
                                        CVisDMatrix vects)
```
    Perform symmetric eigen analysis.
    
    Parameters:
    
    A -
    
    vals -
    
    vects -
    
    Returns:
  - Dsyev
```
public int Dsyev(int m,
                 double[][] a,
                 double[] vals,
                 double[][] vects)
```
    Call the lapack routine
    
    Parameters:
    
    m -
    
    a -
    
    vals -
    
    vects -
    
    Returns:
  - MeanDataItem
```
public final CDVector MeanDataItem()
```
    Returns the mean of all sample vectors.
    
    Returns:
    
    A mean vector.
  - ToFile
```
public void ToFile(java.io.DataOutputStream fh)
```
    Writes a PCA object to a binary file.
    
    Parameters:
    
    fh - file handler to output stream
  - FromFile
```
public void FromFile(java.io.DataInputStream fh)
```
    Reads a PCA object from a binary file.
    
    Parameters:
    
    fh - file handler to open binary file for reading
  - Dump
```
public void Dump(java.lang.String szPath)
```
    Debug method that provides a human readable file dump of the object data. Writes eigen vectors and eigen values to matlab text files: pca_eigenvectors.m pca_eigenvalues.m pca_eigenvalues_org.m
    
    Parameters:
    
    szPath - Path including terminating backslash where the data is dumped.
  - Deform
```
public void Deform(CDVector params,
                   CDVector object)
```
    Projects a set of PC scores to the original space.
    
    Specified by:
    
    Deform in class CAAMDeform
    
    Parameters:
    
    params - PC scores, i.e. the model paramerisation.
    
    object - Resulting projection into the original space, e.g. a shape.
  - MahalanobisDistance
```
public double MahalanobisDistance(CDVector params)
```
    Returns the Mahalanobis distance of a set of PCA parameters.
    
    Parameters:
    
    params - A set of PCA parameters.
    
    Returns:
    
    The Mahalanobis distance.
  - ShuffleData
```
public void ShuffleData()
```
    Shuffle each dimension in all data items over all observations.
  - TruncateParallel
```
public int TruncateParallel()
```
    Truncate eigenvectors and eigenvalues using parallel analysis. Also known as Horn's parallel analysis or Humphrey-Ilgen parallel analysis. Assumes the data items have not been cleared.
    
    Specified by:
    
    TruncateParallel in class CAAMDeform
    
    Returns:
    
    The number of parameters in the truncated basis.
  - TruncateVar
```
public int TruncateVar(double retained_variance)
```
    Truncate eigenvectors and eigenvalues to retain 'retained_variance'. I.e. if retained_variance = .95 this function calculates how many eigenvectors we need to explain 95% of the total variation in the PCA training set.
    
    Specified by:
    
    TruncateVar in class CAAMDeform
    
    Parameters:
    
    retained_variance - Amount of variance to retain [0
    Returns:
    
    The number of parameters in the truncated basis.
  - Truncate
```
public void Truncate(int n)
```
    Truncate eigenvectors and eigenvalues to retain the 'n' largest.
    
    Specified by:
    
    Truncate in class CAAMDeform
    
    Parameters:
    
    n - Amount of eigenvectors/values to retain.
  - CalcNParam
```
public int CalcNParam(double retained_variance)
```
    Calulates the needed number of parameters to retain 'retained_variance'. I.e. if retained_variance = .95 then this function calculates how many eigenvectors we need to explain 95% of the total variation in the PCA training set. Assumes that no eigenvalue cutoff has been done prior to the call.
    
    Parameters:
    
    retained_variance - Amount of variance to retain [0
    Returns:
    
    The number of model parameters.
  - Project
```
public void Project(CDVector obs,
                    CDVector param)
```
    Projects an observation into the PCA space. Notice: Costly, due to the non-cached transpose of the eigenvectors.
    
    Parameters:
    
    obs - Input observation.
    
    param - Output model parameters of the (possibly truncated) basis.
  - BackProject
```
public void BackProject(CDVector param,
                        CDVector synth_obs)
```
    Back projects a set of PCA model parameters into the original space.
    
    Parameters:
    
    param - Input PCA model parameters.
    
    synth_obs - Synthesized output observation.
  - Filter
```
public void Filter(CDVector obs)
```
    Projects an observation into the PCA space and back. Notice: Costly, due to the non-cached transpose of the eigenvectors in the project call.
    
    Parameters:
    
    obs - Input observation.

Class CAAMDeformPCA

Field Summary

Fields inherited from class gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMDeform

Constructor Summary

Method Summary

Methods inherited from class gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMDeform

Methods inherited from class gov.nih.mipav.view.renderer.WildMagic.AAM.CAAMObject

Methods inherited from class java.lang.Object

Field Detail

m_mEigenVectors

m_vEigenValues

m_vEigenValuesOrg

m_vvData

m_vDataMean

Constructor Detail

CAAMDeformPCA

Method Detail

EigenValues

EigenValuesOrg

EigenVectors

NParameters

NParametersOrg

ClearDataItems

NDataItems

ParameterWeight

ParameterWeightOrg

InsertDataItem

UseIdentityTransformation

DoPCA

DoPCA

VisDMatrixSymmetricEigen

Dsyev

MeanDataItem

ToFile

FromFile

Dump

Deform

MahalanobisDistance

ShuffleData

TruncateParallel

TruncateVar

Truncate

CalcNParam

Project

BackProject

Filter