| Canima::Graph< captype, tcaptype, flowtype >::arc | |
| Carguments | |
| ►Canima::B1GammaDistributionIntegrand | Integrand to compute the internal integral per distribution in B1GammaMixtureT2RelaxometryCostFunction |
| Canima::B1GammaDerivativeDistributionIntegrand | Integrand to compute the internal derivative integral per distribution in B1GammaMixtureT2RelaxometryCostFunction |
| Canima::B1GMMDistributionIntegrand | Integrand to compute the internal integral per distribution in B1GMMRelaxometryCostFunction |
| ►Canima::BaseBlockMatcher< TInputImageType > | |
| ►Canima::BaseAffineBlockMatcher< TInputImageType > | |
| Canima::AnatomicalBlockMatcher< TInputImageType > | |
| Canima::MCMBlockMatcher< TInputImageType > | |
| Canima::TensorBlockMatcher< TInputImageType > | |
| Canima::DistortionCorrectionBlockMatcher< TInputImageType > | |
| ►Canima::BaseRootFindingAlgorithm | |
| Canima::BisectionRootFindingAlgorithm | |
| Canima::BoostBisectionRootFindingAlgorithm | |
| Canima::BracketAndSolveRootFindingAlgorithm | |
| Canima::BrentRootFindingAlgorithm | |
| Canima::DekkerRootFindingAlgorithm | |
| Canima::TOMS748RootFindingAlgorithm | |
| ►Canima::BaseTransformAgregator< NDimensions > | |
| Canima::BalooSVFTransformAgregator< NDimensions > | |
| Canima::DenseSVFTransformAgregator< NDimensions > | |
| Canima::LSWTransformAgregator< NDimensions > | |
| Canima::LTSWTransformAgregator< NDimensions > | |
| Canima::MEstTransformAgregator< NDimensions > | |
| Canima::Block< Type > | |
| Canima::Block< node_id > | |
| ►Canima::BlockMatchingInitializer< PixelType, NDimensions >::BlockGeneratorThreadStruct | |
| Canima::MCMBlockMatchingInitializer< PixelType, NDimensions >::MCMBlockGeneratorThreadStruct | |
| Canima::CholeskyDecomposition | Cholesky decomposition: decomposes a symmetric matrix A in the form L D L^T, where L is lower triangular and D diagonal. May be used to solve efficiently any linear system using the solve methods. Refer to Gill, Golub et al. Methods for modifying matrix factorizations. 1974 |
| Canima::NLOPTParametersConstraintFunction::ConstraintDataType | |
| Canima::DawsonIntegrand | |
| Canima::DBlock< Type > | |
| Canima::DBlock< anima::Graph::nodeptr > | |
| Canima::EPGSignalSimulator | |
| Canima::errors_pair_comparator | |
| Canima::BaseProbabilisticTractographyImageFilter< TInputModelImageType >::FiberWorkType | |
| Canima::FuzzyCMeansFilter< ScalarType > | |
| Canima::FuzzyCMeansFilter< double > | |
| Canima::GaussLaguerreQuadrature | Computes the Gauss Laguerre quadrature of a function defined from R^+ into R. Recenters the function on the interest zone with an affine relation, then uses Gauss Legendre on the left out part of the function and computes the main part with Gauss Laguerre |
| Canima::GradientFileReader< GradientType, BValueScalarType > | |
| Canima::Graph< captype, tcaptype, flowtype > | |
| Canima::ImageDataSplitter< TInputImage > | |
| ►CImageToImageFilter | |
| Canima::CheckStructureNeighborFilter< TInput, TMask, TOutput > | Class removing lesions that are not sufficiently in the white matter. Intensity rules may not be enough to discard false positives, therefore we also use localization information. Considering that MS lesions are typically located in WM, we remove the detected ones that do not sufficiently achieve this condition. This filter take two entries: a lesion mask and a white matter map |
| Canima::ComputeMahalanobisImagesFilter< TInputImage, TMaskImage, TOutput > | Compute the mahalanobis images from the NABT model |
| Canima::DistortionCorrectionImageFilter< TInputImage > | |
| Canima::DWISimulatorFromDTIImageFilter< PixelScalarType > | |
| Canima::ExpTensorImageFilter< TScalarType, NDimensions > | |
| Canima::FDRCorrectImageFilter< PixelScalarType > | |
| Canima::GcStremMsLesionsSegmentationFilter< TInputImage > | Class performing lesion segmentation |
| Canima::GeneralizedFAImageFilter< TInputPixelType > | |
| Canima::Graph3DFilter< TInput, TOutput > | Class allowing the decimation of the images if necessary (if 3D graph size causes memory problems). This class just launchs NLinksFilter with appropriate image sizes |
| Canima::GraphCutFilter< TInput, TOutput > | Class performing grah cut segmentation. First the sources and sinks probabilities maps are computed using the TLinkFilter. Then the Graph3DFilter is called to perform the graph cut |
| Canima::ImageClassifierFilter< TInput, TMask, TOutput > | Classify each voxels into one of the given GMM classes |
| Canima::JacobianMatrixImageFilter< TPixelType, TOutputPixelType, Dimension > | Compute the Jacobian matrix in real coordinates of a displacement field |
| Canima::LogTensorImageFilter< TScalarType, NDimensions > | |
| Canima::MajorityLabelVotingImageFilter< TPixelType > | |
| Canima::MCMAverageImagesImageFilter< TPixelType > | |
| Canima::MCMScalarMapsImageFilter< TPixelType > | |
| Canima::MeanAndVarianceImagesFilter< TInputImage, TOutputImage > | Applies an variance filter to an image |
| Canima::MEstimateSVFImageFilter< TScalarType, NDegreesOfFreedom, NDimensions > | |
| Canima::NLinksFilter< TInput, TOutput > | Class creating a 3D graph in a graph cut framework |
| Canima::NonLocalMeansTemporalImageFilter< TInputImage > | |
| ►Canima::NumberedThreadImageToImageFilter< TInputImage, TOutputImage > | Implements a class to handle thread number in a dynamic way for multithreaded methods needing thread numbering even for dynamic threading |
| ►Canima::MaskedImageToImageFilter< TInputImage, TOutputImage > | |
| Canima::T1RelaxometryEstimationImageFilter< TInputImage, TOutputImage > | |
| Canima::T1SERelaxometryEstimationImageFilter< TInputImage, TOutputImage > | |
| Canima::T2EPGRelaxometryEstimationImageFilter< TInputImage, TOutputImage > | |
| Canima::T2RelaxometryEstimationImageFilter< TInputImage, TOutputImage > | |
| Canima::ODFEstimatorImageFilter< TInputPixelType, TOutputPixelType > | |
| Canima::PyramidImageFilter< TInputImage, TOutputImage > | Computes a pyramid of images using the provided resampler to perform resampling |
| Canima::RemoveTouchingBorderFilter< TInput, TMask, TOutput > | Class selecting the connected components touching a given mask border. In MRI, external CSF may contain artifacts due to fluid flow. These effects can cause voxels in the cortex or external CSF to have intensities similar to MS lesions. In order to reduce the number of false positives due to these effects, we remove all candidate lesions that are contiguous to the brain mask border |
| Canima::ResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType > | |
| Canima::SimuBlochCoherentGRE< TImage > | |
| Canima::SimuBlochGRE< TImage > | |
| Canima::SimuBlochIRGRE< TImage > | |
| Canima::SimuBlochIRSE< TImage > | |
| Canima::SimuBlochSE< TImage > | |
| Canima::SimuBlochSPGRE< TImage > | |
| Canima::StimulatedSpinEchoImageFilter< TImage, TOutputImage > | |
| Canima::SVFExponentialImageFilter< TPixelType, Dimension > | Computes the exponentiation of a stationary velocity field using sclaing and squaring and approximated exponential integrators |
| Canima::SVFLieBracketImageFilter< TPixelType, Dimension > | Computes the Lie bracket between two fields u and v as expressed by Bossa et al |
| Canima::TLinksFilter< TInput, TOutput > | Class computing the probability maps that are used to create the t-links |
| ►Canima::NumberedThreadImageToImageFilter< ImageType, itk::Image< double, ImageType::ImageDimension > > | |
| Canima::NoiseGeneratorImageFilter< ImageType > | |
| ►Canima::NumberedThreadImageToImageFilter< itk::Image< InputPixelScalarType, 3 >, itk::VectorImage< OutputPixelScalarType, 3 > > | |
| ►Canima::MaskedImageToImageFilter< itk::Image< InputPixelScalarType, 3 >, itk::VectorImage< OutputPixelScalarType, 3 > > | |
| Canima::DTIEstimationImageFilter< InputPixelScalarType, OutputPixelScalarType > | |
| ►Canima::NumberedThreadImageToImageFilter< itk::Image< InputPixelType, 3 >, anima::MCMImage< OutputPixelType, 3 > > | |
| ►Canima::MaskedImageToImageFilter< itk::Image< InputPixelType, 3 >, anima::MCMImage< OutputPixelType, 3 > > | |
| Canima::MCMEstimatorImageFilter< InputPixelType, OutputPixelType > | |
| ►Canima::NumberedThreadImageToImageFilter< itk::Image< TPixelScalarType, 3 >, itk::Image< TPixelScalarType, 3 > > | |
| ►Canima::MaskedImageToImageFilter< itk::Image< TPixelScalarType, 3 >, itk::Image< TPixelScalarType, 3 > > | |
| Canima::GammaMixtureT2RelaxometryEstimationImageFilter< TPixelScalarType > | |
| Canima::GMMT2RelaxometryEstimationImageFilter< TPixelScalarType > | |
| Canima::MultiT2RelaxometryEstimationImageFilter< TPixelScalarType > | Implements multi-peak T2 relaxometry estimation (with or without regularization) |
| ►Canima::NumberedThreadImageToImageFilter< itk::VectorImage< double, ImageDimension >, itk::Image< double, ImageDimension > > | |
| Canima::DTIScalarMapsImageFilter< ImageDimension > | Applies an variance filter to an image |
| ►Canima::NumberedThreadImageToImageFilter< itk::VectorImage< PixelScalarType, 3 >, itk::Image< PixelScalarType, 3 > > | |
| ►Canima::MaskedImageToImageFilter< itk::VectorImage< PixelScalarType, 3 >, itk::Image< PixelScalarType, 3 > > | |
| Canima::LocalPatchCovarianceDistanceImageFilter< PixelScalarType > | |
| Canima::LocalPatchMeanDistanceImageFilter< PixelScalarType > | |
| Canima::NLMeansPatientToGroupComparisonImageFilter< PixelScalarType > | |
| ►Canima::PatientToGroupComparisonImageFilter< PixelScalarType > | Implements patient to group comparison as in http://dx.doi.org/10.1007/978-3-540-85988-8_116 |
| Canima::PatientToGroupODFComparisonImageFilter< PixelScalarType > | |
| ►Canima::NumberedThreadImageToImageFilter< itk::VectorImage< TPixelType, TImageDimension >, itk::VectorImage< TPixelType, TImageDimension > > | |
| ►Canima::MaskedImageToImageFilter< itk::VectorImage< TPixelType, TImageDimension >, itk::VectorImage< TPixelType, TImageDimension > > | |
| Canima::FlipTensorImageFilter< TPixelType, TImageDimension > | |
| ►Canima::NumberedThreadImageToImageFilter< TImageType, TImageType > | |
| ►Canima::MaskedImageToImageFilter< TImageType, TImageType > | |
| ►Canima::OrientedModelBaseResampleImageFilter< TImageType, TInterpolatorPrecisionType > | |
| Canima::MCMResampleImageFilter< TImageType, TInterpolatorPrecisionType > | |
| Canima::ODFResampleImageFilter< TImageType, TInterpolatorPrecisionType > | |
| Canima::TensorResampleImageFilter< TImageType, TInterpolatorPrecisionType > | |
| ►Canima::NumberedThreadImageToImageFilter< TInputImage, TInputImage > | |
| Canima::NonLocalMeansImageFilter< TInputImage > | |
| ►Canima::NumberedThreadImageToImageFilter< TLabelImage, TLabelImage > | |
| Canima::SegmentationMeasuresImageFilter< TLabelImage > | |
| ►CImageToImageMetric | |
| Canima::FastCorrelationImageToImageMetric< TFixedImage, TMovingImage > | |
| Canima::FastMeanSquaresImageToImageMetric< TFixedImage, TMovingImage > | |
| ►CInPlaceImageFilter | |
| Canima::BalooExternalExtrapolateImageFilter< TScalarType, NDegreesOfFreedom, NDimensions > | |
| Canima::RecursiveLineYvvGaussianImageFilter< TInputImage, TOutputImage > | |
| Canima::SmoothingRecursiveYvvGaussianImageFilter< TInputImage, TOutputImage > | |
| CIntegrandType | |
| ►CInterpolateImageFunction | |
| Canima::MCMLinearInterpolateImageFunction< TInputImage, TCoordRep > | |
| Canima::VectorModelLinearInterpolateImageFunction< TInputImage, TCoordRep > | |
| Canima::KMeansFilter< DataType, PointDimension > | |
| Canima::KummerIntegrand | |
| CLabelSetMeasures | Metrics stored per label |
| ►CLightObject | |
| ►Canima::BaseCompartment | |
| ►Canima::BaseIsotropicCompartment | |
| Canima::FreeWaterCompartment | |
| Canima::IsotropicRestrictedWaterCompartment | |
| Canima::StationaryWaterCompartment | |
| Canima::NODDICompartment | |
| Canima::StaniszCompartment | |
| Canima::StickCompartment | |
| Canima::TensorCompartment | |
| Canima::ZeppelinCompartment | |
| ►Canima::BaseMCMCost | Base cost function class to handle maximum likelihood estimation |
| Canima::GaussianMCMCost | Class for computing marginal and profile costs and derivatives. This is not thread safe at all so be sure to intantiate one per thread |
| Canima::GaussianMCMVariableProjectionCost | Class for computing variable projection costs and derivatives. Right now, it is only available for Gaussian noise. By the way, this is not thread safe at all so be sure to intantiate one per thread |
| Canima::LogEuclideanTensorCalculator< TScalarType > | |
| Canima::MCML2DistanceComputer | Computes a L2 distance between two MCM of any type |
| Canima::MCMWeightedAverager | Computes a weighted average of input multi-compartment models. The output model is at the same time giving the number and type of output compartments but also its parameters are erased when performing Update to get the result |
| Canima::MultiCompartmentModel | MultiCompartmentModel: holds several diffusion compartments, ordered by type It also handles weights of the different compartments. Although there are N weights in memory for N compartments, the parameters returned and set are considered to have one less weight in them, the first one being removed. This comes from the fact that they sum up to 1 |
| Canima::LowMemoryLocalPatchCovarianceDistanceBridge | |
| Canima::LowMemoryLocalPatchMeanDistanceBridge | |
| Canima::LowMemoryNLMeansPatientToGroupComparisonBridge | |
| Canima::LowMemoryPatientToGroupComparisonBridge | |
| Canima::LowMemoryPatientToGroupODFComparisonBridge | |
| Canima::MatrixLoggerFilter< TInputScalarType, TOutputScalarType, NDimensions, NDegreesOfFreedom > | Class to compute many log-vectors in a multi-threaded way |
| ►CMatrixOffsetTransformBase | |
| Canima::AxisRotationTransform< TScalarType > | |
| ►Canima::DirectionScaleSkewTransform< TScalarType > | |
| Canima::DirectionScaleTransform< TScalarType > | |
| Canima::DirectionTransform< TScalarType > | |
| Canima::LogRigid3DTransform< TScalarType > | |
| Canima::SplitAffine3DTransform< TScalarType > | |
| Canima::SymmetryPlaneTransform< TScalarType > | |
| Canima::MCMFileReader< PixelType, ImageDimension > | |
| Canima::MCMFileWriter< PixelType, ImageDimension > | |
| Canima::MultiCompartmentModelCreator | Really this class is some simplified factory that creates the MCM that it knows |
| ►CMultipleValuedCostFunction | |
| Canima::GaussianMCMVariableProjectionMultipleValuedCostFunction | |
| Canima::MCMMultipleValuedCostFunction | |
| ►CMultipleValuedNonLinearOptimizer | |
| Canima::BoundedLevenbergMarquardtOptimizer | Levenberg-Marquardt optimizer with lower and upper bounds on parameters Implementation of the original algorithmm, very well described in K. Madsen, H.B. Nielsen and O. Tingleff. Methods for non-linear least squares problems. 2004 http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf Bounded version by projection as suggested by Kanzow et al. but with a lot of modifications, including calculation of optimal lambda C. Kanzow, N. Yamashita and M. Fukushima. Levenberg-Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints. Journal of computational and applied mathematics. 172:375-397, 2004 |
| Canima::Graph< captype, tcaptype, flowtype >::node | |
| Canima::Graph< captype, tcaptype, flowtype >::nodeptr | |
| ►Canima::NonLocalPatchBaseSearcher< ImageType > | |
| Canima::NonLocalMeansPatchSearcher< ImageType, DataImageType > | |
| Canima::NonLocalT2DistributionPatchSearcher< ImageType, DataImageType > | |
| ►Canima::NonLocalPatchBaseSearcher< itk::VectorImage< ImageScalarType, DataImageType::ImageDimension > > | |
| Canima::NLMeansVectorPatchSearcher< ImageScalarType, DataImageType > | |
| ►CObject | |
| ►Canima::BlockMatchingInitializer< PixelType, NDimensions > | |
| Canima::MCMBlockMatchingInitializer< PixelType, NDimensions > | |
| Canima::NLOPTParametersConstraintFunction | |
| Canima::ODFSphericalHarmonicBasis | |
| Canima::DTIEstimationImageFilter< InputPixelScalarType, OutputPixelScalarType >::OptimizationDataStructure | |
| ►COptimizer | |
| Canima::BVLSOptimizer | Bounded variable least squares optimizer. Coming from Stark and Parker paper P.B. Stark and R.L. Parker. Bounded-variable least-squares: an algorithm and applications. Computational Statistics, 1995 |
| Canima::NNLSOptimizer | Non negative least squares optimizer. Implements Lawson et al method, of squared problem is activated, assumes we pass AtA et AtB and uses Bro and de Jong method |
| Canima::BlockMatchingInitializer< PixelType, NDimensions >::pair_comparator | |
| Canima::BaseProbabilisticTractographyImageFilter< TInputModelImageType >::pair_comparator | |
| Canima::pair_decreasing_comparator | |
| Canima::pair_increasing_comparator | |
| ►CProcessObject | |
| ►Canima::BaseBMRegistrationMethod< TInputImageType > | |
| Canima::AsymmetricBMRegistrationMethod< TInputImageType > | |
| Canima::DistortionCorrectionBMRegistrationMethod< TInputImageType > | |
| Canima::KissingSymmetricBMRegistrationMethod< TInputImageType > | |
| Canima::SymmetricBMRegistrationMethod< TInputImageType > | |
| Canima::BaseProbabilisticTractographyImageFilter< TInputModelImageType > | |
| ►Canima::BaseTractographyImageFilter | |
| Canima::dtiTractographyImageFilter | DTI tractography image filter. Simple step by step tratpography, using advection-diffusion tricks from Weinstein et al. 1999. Tensorlines: Advection-Diffusion based Propagation through Diffusion Tensor Fields |
| Canima::ClassificationStrategy< TInputImage, TMaskImage > | |
| Canima::ComputeSolution< TInputImage, TMaskImage, TAtlasImage > | Class computing the 3-class GMM respresenting the NABT, where each Gaussian represents one of the brain tissues WM, GM and CSF. First a model initializer is launched, then the REM algorithm is performed using this initialization. The NABT model can be written in a csv file |
| ►Canima::GaussianEMEstimator< TInputImage, TMaskImage > | Gaussian Model estimator Class performing expectation-maximation algorithm |
| Canima::GaussianREMEstimator< TInputImage, TMaskImage > | Class performing a robust expectation-maximation (REM) algorithm. Allow finding the 3-classes NABT model estimation |
| ►Canima::ModelInitializer | Gaussian model initializers Model Initializer represents the processes computing a gaussian model that will be used as the model initialization in an EM process |
| Canima::AtlasInitializer< TInputImage, TMaskImage, TAtlasImage > | |
| Canima::HierarchicalInitializer< TInputImage, TMaskImage > | Class initializing a gaussian mixture with hierarchical information It uses 'a priori' knowledge of the sequences |
| Canima::RandomInitializer | Class initializing ramdomly a gaussian model |
| Canima::PyramidalBlockMatchingBridge< ImageDimension > | |
| Canima::PyramidalDenseMCMSVFMatchingBridge< ImageDimension > | |
| Canima::PyramidalDenseSVFMatchingBridge< ImageDimension > | |
| Canima::PyramidalDenseTensorSVFMatchingBridge< ImageDimension > | |
| Canima::PyramidalDistortionCorrectionBlockMatchingBridge< ImageDimension > | |
| Canima::PyramidalSymmetryBridge< PixelType, ScalarType > | |
| Canima::PyramidalSymmetryConstrainedRegistrationBridge< ScalarType > | |
| ►Canima::BaseProbabilisticTractographyImageFilter< itk::VectorImage< double, 3 > > | |
| Canima::DTIProbabilisticTractographyImageFilter | |
| Canima::ODFProbabilisticTractographyImageFilter | |
| Canima::RootFindingFunctionBoostBridge | |
| Canima::RootToleranceBoostBridge | |
| Canima::NLMeansPatientToGroupComparisonImageFilter< PixelScalarType >::SampleIdentifier | |
| Canima::SegPerfApp | Main class to structure application and handle command line options |
| Canima::SegPerfCAnalyzer | Class to compute various metrics to evaluate segmentation results |
| Canima::SegPerfLabelSetMeasures | |
| Canima::SegPerfResults | Class to format and saves results |
| Canima::ShapesReader | |
| Canima::ShapesWriter | |
| ►CSingleValuedCostFunction | |
| Canima::ApproximateMCMSmoothingCostFunction | |
| Canima::B1GammaMixtureT2RelaxometryCostFunction | |
| Canima::B1GMMRelaxometryCostFunction | Cost function for estimating B1 from T2 relaxometry acquisition, following a multi-T2 EPG decay model. The cost function includes (via variable projection) estimation of compartment weights |
| Canima::B1T2RelaxometryDistributionCostFunction | Cost function for estimating B1 from T2 relaxometry acquisition, following a multi-T2 EPG decay model |
| Canima::BaseOrientedModelImageToImageMetric< TFixedImage, TMovingImage > | |
| Canima::BLMLambdaCostFunction | Levenberg-Marquardt lambda update cost function (phi) used for bounded levenberg marquardt optimizer |
| Canima::GaussianMCMVariableProjectionSingleValuedCostFunction | |
| Canima::MCMSingleValuedCostFunction | |
| Canima::MultiT2EPGRelaxometryCostFunction | Cost function for estimating B1 from T2 relaxometry acquisition, following a multi-T2 EPG decay model |
| Canima::MultiTensorSmoothingCostFunction | |
| Canima::ODFMaximaCostFunction | |
| Canima::T1SERelaxometryCostFunction | |
| Canima::T2EPGRelaxometryCostFunction | |
| Canima::T2RelaxometryCostFunction | |
| ►Canima::BaseOrientedModelImageToImageMetric< anima::MCMImage< TFixedImagePixelType, ImageDimension >, anima::MCMImage< TMovingImagePixelType, ImageDimension > > | |
| Canima::MCMCorrelationImageToImageMetric< TFixedImagePixelType, TMovingImagePixelType, ImageDimension > | |
| Canima::MCMMeanSquaresImageToImageMetric< TFixedImagePixelType, TMovingImagePixelType, ImageDimension > | |
| Canima::MCMPairingMeanSquaresImageToImageMetric< TFixedImagePixelType, TMovingImagePixelType, ImageDimension > | |
| Canima::MTPairingCorrelationImageToImageMetric< TFixedImagePixelType, TMovingImagePixelType, ImageDimension > | Multi-tensor correlation similarity measure as defined by Taquet et al, based on pairing of the individual compartments |
| ►Canima::BaseOrientedModelImageToImageMetric< itk::VectorImage< TFixedImagePixelType, ImageDimension >, itk::VectorImage< TMovingImagePixelType, ImageDimension > > | |
| Canima::TensorCorrelationImageToImageMetric< TFixedImagePixelType, TMovingImagePixelType, ImageDimension > | Tensor correlation similarity measure as defined by Taquet et al |
| Canima::TensorGeneralizedCorrelationImageToImageMetric< TFixedImagePixelType, TMovingImagePixelType, ImageDimension > | |
| Canima::TensorMeanSquaresImageToImageMetric< TFixedImagePixelType, TMovingImagePixelType, ImageDimension > | |
| ►CSingleValuedNonLinearOptimizer | |
| Canima::BobyqaOptimizer | BOBYQA Optimizer |
| Canima::NLOPTOptimizers | Implements an ITK wrapper for the NLOPT library |
| Canima::VoxelExhaustiveOptimizer | |
| Canima::SpectralClusteringFilter< ScalarType > | Provides an implementation of spectral clustering, as proposed in A.Y. Ng, M.I. Jordan and Y. Weiss. "On Spectral Clustering: Analysis and an Algorithm." Advances in Neural Information Processing Systems 14. 2001 |
| Canima::SpectralClusteringFilter< double > | |
| Canima::SphericalHarmonic | |
| Canima::MatrixLoggerFilter< TInputScalarType, TOutputScalarType, NDimensions, NDegreesOfFreedom >::ThreadedLogData | |
| Canima::BaseBlockMatcher< TInputImageType >::ThreadedMatchData | |
| CThreaderArguments | |
| Canima::BaseTractographyImageFilter::trackerArguments | |
| Canima::BaseProbabilisticTractographyImageFilter< TInputModelImageType >::trackerArguments | |
| Canima::TransformSeriesReader< TScalarType, NDimensions >::TransformInformation | |
| Canima::TransformSeriesReader< TScalarType, NDimensions > | |
| ►CVectorImage | |
| Canima::MCMImage< TPixel, VImageDimension > | |
| Canima::ODFProbabilisticTractographyImageFilter::XYZ | |
