java.lang.Object

java.lang.Enum<NIFTIIntent>

com.xinapse.multisliceimage.Analyze.NIFTIIntent

All Implemented Interfaces:: Serializable, Comparable<NIFTIIntent>, Constable

public enum NIFTIIntent extends Enum<NIFTIIntent>

This class represents the NIFTI intents.

For probability distributions, most distributions have a number of parameters, below denoted by p1, p2, and p3, and stored in:

intent_p1, intent_p2, intent_p3 if dataset doesn't have 5th dimension
image data array, if dataset does have 5th dimension

Functions to compute with many of the distributions below can be found in the CDF library from U Texas.

Formulas for and discussions of these distributions can be found in the following books. These references are abbreviated to [U, C1 and C2] in the documentation below.

[U] Univariate Discrete Distributions, NL Johnson, S Kotz, AW Kemp.
[C1] Continuous Univariate Distributions, vol. 1, NL Johnson, S Kotz, N Balakrishnan.
[C2] Continuous Univariate Distributions, vol. 2, NL Johnson, S Kotz, N Balakrishnan.

Nested Class Summary

Nested classes/interfaces inherited from class java.lang.Enum
Enum.EnumDesc<E extends Enum<E>>
Enum Constant Summary

Enum Constants

Enum Constant

Description

BETA

[C2, chap 25] Beta distribution (2 params): p1=a, p2=b.

BINOM

[U, chap 3] Binomial distribution (2 params):
p1 = number of trials, p2 = probability per trial.
Prob(x) = (p1 choose x) * p2^x * (1-p2)^(p1-x), for x=0,1,...,p1.

CHI

[C1, chap 18] Chi distribution (1 param): p1 = DOF.

CHISQ

[C1, chap 18] Chi-squared (1 param): p1 = DOF.

CHISQ_NONC

[C2, chap 29] Noncentral chi-squared statistic (2 params):
p1 = DOF, p2 = noncentrality parameter.

CORREL

[C2, chap 32] Correlation coefficient R (1 param):
p1 = degrees of freedom;
R/sqrt(1-R*R) is t-distributed with p1 DOF.

DISPVECT

To signify that the vector value at each voxel is to be taken as a displacement field or vector:
- dataset must have a 5th dimension
- intent must be DISPVECT
- dim[5] must be the dimensionality of the displacment vector (e.g., 3 for spatial displacement, 2 for in-plane).
This intent is specifically for displacements.

ESTIMATE

To signify that the value at each voxel is an estimate of some parameter, set intent_code = NIFTI_INTENT_ESTIMATE.

EXTVAL

[C2, chap 22] Extreme value type I (2 params):
p1 = location, p2 = scale
cdf(x) = exp(-exp(-(x-p1)/p2)).

FTEST

[C2, chap 27] Fisher F statistic (2 params):
p1 = numerator DOF,
p2 = denominator DOF.

FTEST_NONC

[C2, chap 30] Noncentral F statistic (3 params):
p1 = numerator DOF, p2 = denominator DOF,
p3 = numerator noncentrality parameter.

GAMMA

[C1, chap 17] Gamma distribution (2 params):
p1 = shape, p2 = scale.
Density(x) proportional to x^(p1-1) * exp(-p2*x).

GENMATRIX

To store an M x N matrix at each voxel:
- dataset must have a 5th dimension (dim[0]=5 and dim[5]>1)
- intent must be GENMATRIX
- dim[5] must be M*N
- intent_p1 must be M (in float format)
- intent_p2 must be N (ditto)
- the matrix values A[i][[j] are stored in row-order:
- A[0][0] A[0][1] ...

INVGAUSS

[C1, chap 15] Inverse Gaussian (2 params):
p1 = mu, p2 = lambda
Density(x) proportional to exp(-p2*(x-p1)^2/(2*p1^2*x)) / x^3 for x > 0.

LABEL

To signify that the value at each voxel is an index into some set of labels, set intent_code = NIFTI_INTENT_LABEL.

LAPLACE

[C2, chap 24] Laplace distribution (2 params):
p1 = location, p2 = scale.
Density(x) proportional to exp(-abs(x-p1)/p2).

LOGISTIC

[C2, chap 23] Logistic distribution (2 params):
p1 = location, p2 = scale.
Density(x) proportional to sech^2((x-p1)/(2*p2)).

NEURONAME

To signify that the value at each voxel is an index into the NeuroNames labels set, set intent_code = NIFTI_INTENT_NEURONAME.

NONE

The default: no intention is indicated in the header.

NORMAL

[C1, chap 13] Normal distribution (2 params):
p1 = mean, p2 = standard deviation.

POINTSET

To signify that the vector value at each voxel is really a spatial coordinate (e.g., the vertices or nodes of a surface mesh):
- dataset must have a 5th dimension
- intent must be POINTSET
- dim[0] = 5
- dim[1] = number of points
- dim[2] = dim[3] = dim[4] = 1
- dim[5] must be the dimensionality of space (e.g., 3 => 3D space).
- intent_name may describe the object these points come from (e.g., "pial", "gray/white" , "EEG", "MEG").

POISSON

[U, chap 4] Poisson distribution (1 param): p1 = mean.
Prob(x) = exp(-p1) * p1^x / x! , for x=0,1,2,....

PVAL

Data is a 'p-value' (no params).

QUATERNION

To signify that the vector value at each voxel is a quaternion:
- dataset must have a 5th dimension
- intent must be QUATERNION
- dim[0] = 5
- dim[5] = 4
- datatype should be a floating point type.

SYMMATRIX

To store an NxN symmetric matrix at each voxel:
- dataset must have a 5th dimension
- intent must be SYMMATRIX
- dim[5] must be N*(N+1)/2
- intent_p1 must be N (in float format)
- the matrix values A[i][[j] are stored in row-order:
- A[0][0]
- A[1][0] A[1][1]
- A[2][0] A[2][1] A[2][2]
- etc.: row-by-row.

TRIANGLE

To signify that the vector value at each voxel is really a triple of indexes (e.g., forming a triangle) from a pointset dataset:
- dataset must have a 5th dimension
- intent must be TRIANGLE
- dim[0] = 5
- dim[1] = number of triangles
- dim[2] = dim[3] = dim[4] = 1
- dim[5] = 3
- datatype should be an integer type (preferably INT)
- the data values are indexes (0,1,...) into a pointset dataset.

TTEST

[C2, chap 28] Student t statistic (1 param): p1 = DOF.

TTEST_NONC

[C2, chap 31] Noncentral t statistic (2 params):
p1 = DOF, p2 = noncentrality parameter.

UNIFORM

[C2, chap 26] Uniform distribution: p1 = lower end, p2 = upper end.

VECTOR

To signify that the vector value at each voxel is to be taken as a displacement field or vector:
- dataset must have a 5th dimension
- intent must be NIFTI_INTENT_DISPVECT
- dim[5] must be the dimensionality of the displacment vector (e.g., 3 for spatial displacement, 2 for in-plane).
This intent is for general vectors.

WEIBULL

[C1, chap 21] Weibull distribution (3 params):
p1 = location, p2 = scale, p3 = power.
Density(x) proportional to ((x-p1)/p2)^(p3-1) * exp(-((x-p1)/p2)^p3) for x > p1.

ZSCORE

[C1, chap 13] Standard normal (0 params): Density = N(0,1).
Method Summary

Modifier and Type

Method

Description

boolean

isStatistic()

Indicates whether this NIFTIIntent is a statistic intent.

static void

main(String[] args)

Run a self-test on the NIFTIIntent class.

String

toString()

Returns a string describing this NIFTIIntent.

static NIFTIIntent

valueOf(String name)

Returns the enum constant of this class with the specified name.

static NIFTIIntent[]

values()

Returns an array containing the constants of this enum class, in the order they are declared.

Methods inherited from class java.lang.Enum
clone, compareTo, describeConstable, equals, finalize, getDeclaringClass, hashCode, name, ordinal, valueOf

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

Enum Constant Details
- NONE
  
  public static final NIFTIIntent NONE
  
  The default: no intention is indicated in the header.
- CORREL
  
  public static final NIFTIIntent CORREL
  
  [C2, chap 32] Correlation coefficient R (1 param):
  p1 = degrees of freedom;
  R/sqrt(1-R*R) is t-distributed with p1 DOF.
- TTEST
  
  public static final NIFTIIntent TTEST
  
  [C2, chap 28] Student t statistic (1 param): p1 = DOF.
- FTEST
  
  public static final NIFTIIntent FTEST
  
  [C2, chap 27] Fisher F statistic (2 params):
  p1 = numerator DOF,
  p2 = denominator DOF.
- ZSCORE
  
  public static final NIFTIIntent ZSCORE
  
  [C1, chap 13] Standard normal (0 params): Density = N(0,1).
- CHISQ
  
  public static final NIFTIIntent CHISQ
  
  [C1, chap 18] Chi-squared (1 param): p1 = DOF. Density(x) proportional to exp(-x/2) * x^(p1/2-1).
- BETA
  
  public static final NIFTIIntent BETA
  
  [C2, chap 25] Beta distribution (2 params): p1=a, p2=b. Density(x) proportional to x^(a-1) * (1-x)^(b-1).
- BINOM
  
  public static final NIFTIIntent BINOM
  
  [U, chap 3] Binomial distribution (2 params):
  p1 = number of trials, p2 = probability per trial.
  Prob(x) = (p1 choose x) * p2^x * (1-p2)^(p1-x), for x=0,1,...,p1.
- GAMMA
  
  public static final NIFTIIntent GAMMA
  
  [C1, chap 17] Gamma distribution (2 params):
  p1 = shape, p2 = scale.
  Density(x) proportional to x^(p1-1) * exp(-p2*x).
- POISSON
  
  public static final NIFTIIntent POISSON
  
  [U, chap 4] Poisson distribution (1 param): p1 = mean.
  Prob(x) = exp(-p1) * p1^x / x! , for x=0,1,2,....
- NORMAL
  
  public static final NIFTIIntent NORMAL
  
  [C1, chap 13] Normal distribution (2 params):
  p1 = mean, p2 = standard deviation.
- FTEST_NONC
  
  public static final NIFTIIntent FTEST_NONC
  
  [C2, chap 30] Noncentral F statistic (3 params):
  p1 = numerator DOF, p2 = denominator DOF,
  p3 = numerator noncentrality parameter.
- CHISQ_NONC
  
  public static final NIFTIIntent CHISQ_NONC
  
  [C2, chap 29] Noncentral chi-squared statistic (2 params):
  p1 = DOF, p2 = noncentrality parameter.
- LOGISTIC
  
  public static final NIFTIIntent LOGISTIC
  
  [C2, chap 23] Logistic distribution (2 params):
  p1 = location, p2 = scale.
  Density(x) proportional to sech^2((x-p1)/(2*p2)).
- LAPLACE
  
  public static final NIFTIIntent LAPLACE
  
  [C2, chap 24] Laplace distribution (2 params):
  p1 = location, p2 = scale.
  Density(x) proportional to exp(-abs(x-p1)/p2).
- UNIFORM
  
  public static final NIFTIIntent UNIFORM
  
  [C2, chap 26] Uniform distribution: p1 = lower end, p2 = upper end.
- TTEST_NONC
  
  public static final NIFTIIntent TTEST_NONC
  
  [C2, chap 31] Noncentral t statistic (2 params):
  p1 = DOF, p2 = noncentrality parameter.
- WEIBULL
  
  public static final NIFTIIntent WEIBULL
  
  [C1, chap 21] Weibull distribution (3 params):
  p1 = location, p2 = scale, p3 = power.
  Density(x) proportional to ((x-p1)/p2)^(p3-1) * exp(-((x-p1)/p2)^p3) for x > p1.
- CHI
  
  public static final NIFTIIntent CHI
  
  [C1, chap 18] Chi distribution (1 param): p1 = DOF. Density(x) proportional to x^(p1-1) * exp(-x^2/2) for x > 0.
  p1 = 1 = 'half normal' distribution
  p1 = 2 = Rayleigh distribution
  p1 = 3 = Maxwell-Boltzmann distribution.
- INVGAUSS
  
  public static final NIFTIIntent INVGAUSS
  
  [C1, chap 15] Inverse Gaussian (2 params):
  p1 = mu, p2 = lambda
  Density(x) proportional to exp(-p2*(x-p1)^2/(2*p1^2*x)) / x^3 for x > 0.
- EXTVAL
  
  public static final NIFTIIntent EXTVAL
  
  [C2, chap 22] Extreme value type I (2 params):
  p1 = location, p2 = scale
  cdf(x) = exp(-exp(-(x-p1)/p2)).
- PVAL
  
  public static final NIFTIIntent PVAL
  
  Data is a 'p-value' (no params).
- ESTIMATE
  
  public static final NIFTIIntent ESTIMATE
  
  To signify that the value at each voxel is an estimate of some parameter, set intent_code = NIFTI_INTENT_ESTIMATE. The name of the parameter may be stored in intent_name.
- LABEL
  
  public static final NIFTIIntent LABEL
  
  To signify that the value at each voxel is an index into some set of labels, set intent_code = NIFTI_INTENT_LABEL. The filename with the labels may stored in aux_file.
- NEURONAME
  
  public static final NIFTIIntent NEURONAME
  
  To signify that the value at each voxel is an index into the NeuroNames labels set, set intent_code = NIFTI_INTENT_NEURONAME.
- GENMATRIX
  
  public static final NIFTIIntent GENMATRIX
  
  To store an M x N matrix at each voxel:
  - dataset must have a 5th dimension (dim[0]=5 and dim[5]>1)
  - intent must be GENMATRIX
  - dim[5] must be M*N
  - intent_p1 must be M (in float format)
  - intent_p2 must be N (ditto)
  - the matrix values A[i][[j] are stored in row-order:
  - A[0][0] A[0][1] ... A[0][N-1]
  - A[1][0] A[1][1] ... A[1][N-1]
  - etc., until
  - A[M-1][0] A[M-1][1] ... A[M-1][N-1].
- SYMMATRIX
  
  public static final NIFTIIntent SYMMATRIX
  
  To store an NxN symmetric matrix at each voxel:
  - dataset must have a 5th dimension
  - intent must be SYMMATRIX
  - dim[5] must be N*(N+1)/2
  - intent_p1 must be N (in float format)
  - the matrix values A[i][[j] are stored in row-order:
  - A[0][0]
  - A[1][0] A[1][1]
  - A[2][0] A[2][1] A[2][2]
  - etc.: row-by-row.
- DISPVECT
  
  public static final NIFTIIntent DISPVECT
  
  To signify that the vector value at each voxel is to be taken as a displacement field or vector:
  - dataset must have a 5th dimension
  - intent must be DISPVECT
  - dim[5] must be the dimensionality of the displacment vector (e.g., 3 for spatial displacement, 2 for in-plane).
  This intent is specifically for displacements.
- VECTOR
  
  public static final NIFTIIntent VECTOR
  
  To signify that the vector value at each voxel is to be taken as a displacement field or vector:
  - dataset must have a 5th dimension
  - intent must be NIFTI_INTENT_DISPVECT
  - dim[5] must be the dimensionality of the displacment vector (e.g., 3 for spatial displacement, 2 for in-plane).
  This intent is for general vectors.
- POINTSET
  
  public static final NIFTIIntent POINTSET
  
  To signify that the vector value at each voxel is really a spatial coordinate (e.g., the vertices or nodes of a surface mesh):
  - dataset must have a 5th dimension
  - intent must be POINTSET
  - dim[0] = 5
  - dim[1] = number of points
  - dim[2] = dim[3] = dim[4] = 1
  - dim[5] must be the dimensionality of space (e.g., 3 => 3D space).
  - intent_name may describe the object these points come from (e.g., "pial", "gray/white" , "EEG", "MEG").
- TRIANGLE
  
  public static final NIFTIIntent TRIANGLE
  
  To signify that the vector value at each voxel is really a triple of indexes (e.g., forming a triangle) from a pointset dataset:
  - dataset must have a 5th dimension
  - intent must be TRIANGLE
  - dim[0] = 5
  - dim[1] = number of triangles
  - dim[2] = dim[3] = dim[4] = 1
  - dim[5] = 3
  - datatype should be an integer type (preferably INT)
  - the data values are indexes (0,1,...) into a pointset dataset.
- QUATERNION
  
  public static final NIFTIIntent QUATERNION
  
  To signify that the vector value at each voxel is a quaternion:
  - dataset must have a 5th dimension
  - intent must be QUATERNION
  - dim[0] = 5
  - dim[5] = 4
  - datatype should be a floating point type.
Method Details
- values
  
  public static NIFTIIntent[] values()
  
  Returns an array containing the constants of this enum class, in the order they are declared.
  
  Returns:
  
  an array containing the constants of this enum class, in the order they are declared
- valueOf
  
  public static NIFTIIntent valueOf(String name)
  
  Returns the enum constant of this class with the specified name. The string must match exactly an identifier used to declare an enum constant in this class. (Extraneous whitespace characters are not permitted.)
  
  Parameters:
  
  name - the name of the enum constant to be returned.
  
  Returns:
  
  the enum constant with the specified name
  
  Throws:
  
  IllegalArgumentException - if this enum class has no constant with the specified name
  
  NullPointerException - if the argument is null
- isStatistic
  
  public boolean isStatistic()
  
  Indicates whether this NIFTIIntent is a statistic intent.
  
  Returns:
  
  true if this is a statistic intent.
- toString
  
  public String toString()
  
  Returns a string describing this NIFTIIntent.
  
  Overrides:
  
  toString in class Enum<NIFTIIntent>
  
  Returns:
  
  a string describing this NIFTIIntent.
- main
  
  public static void main(String[] args)
  
  Run a self-test on the NIFTIIntent class.
  
  Parameters:
  
  args - ignored.

Enum Class NIFTIIntent

Nested Class Summary

Nested classes/interfaces inherited from class java.lang.Enum

Enum Constant Summary

Method Summary

Methods inherited from class java.lang.Enum

Methods inherited from class java.lang.Object

Enum Constant Details

NONE

CORREL

TTEST

FTEST

ZSCORE

CHISQ

BETA

BINOM

GAMMA

POISSON

NORMAL

FTEST_NONC

CHISQ_NONC

LOGISTIC

LAPLACE

UNIFORM

TTEST_NONC

WEIBULL

CHI

INVGAUSS

EXTVAL

PVAL

ESTIMATE

LABEL

NEURONAME

GENMATRIX

SYMMATRIX

DISPVECT

VECTOR

POINTSET

TRIANGLE

QUATERNION

Method Details

values

valueOf

isStatistic

toString

main