Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
BRISC::Matrix::CholeskyDecompositionCholesky Decomposition. For a symmetric, positive definite matrix A, the Cholesky decomposition is an lower triangular matrix L so that A = L*L'. If the matrix is not symmetric or positive definite, the constructor returns a partial decomposition and sets an internal flag that may be queried by the isSPD() method
BRISC::Core::CombineCoOccurrenceHelper class for co-occurrence calculation
BRISC::Matrix::EigenvalueDecompositionEigenvalues and eigenvectors of a real matrix. If A is symmetric, then A = V*D*V' where the eigenvalue matrix D is diagonal and the eigenvector matrix V is orthogonal. I.e. A = V.Multiply(D.Multiply(V.Transpose())) and V.Multiply(V.Transpose()) equals the identity matrix. If A is not symmetric, then the eigenvalue matrix D is block diagonal with the real eigenvalues in 1-by-1 blocks and any complex eigenvalues, lambda + i*mu, in 2-by-2 blocks, [lambda, mu; -mu, lambda]. The columns of V represent the eigenvectors in the sense that A*V = V*D, i.e. A.Multiply(V) equals V.Multiply(D). The matrix V may be badly conditioned, or even singular, so the validity of the equation A = V*D*Inverse(V) depends upon V.cond()
BRISC::GUI::FeatureVectorDialogFeature vector selection dialog
BRISC::Core::GaborFilterPerforms Gabor filtering operations on a LIDCNodule
BRISC::Matrix::GeneralMatrix.NET GeneralMatrix class
BRISC::Core::GlobalCoOccurrenceProvides methods for calculating Haralick features on LIDCNodule objects
BRISC::Core::LIDCImportConverts LIDC data into .srs and .xml files for series and nodule viewers
BRISC::Core::LIDCNoduleRepresents a LIDC database nodule
BRISC::Core::LIDCNodule::PhysicalSizeStructure for representing the physical size of a nodule
BRISC::Core::LIDCNoduleDBA collection of LIDCNodule objects
BRISC::GUI::ListViewColumnSorterCustom sorter class for nodule list views
BRISC::Matrix::LUDecompositionLU Decomposition. For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n unit lower triangular matrix L, an n-by-n upper triangular matrix U, and a permutation vector piv of length m so that A(piv,:) = L*U.
 If m &lt; n, then L is m-by-m and U is m-by-n. </code>
            The LU decompostion with pivoting always exists, even if the matrix is
            singular, so the constructor will never fail.  The primary use of the
            LU decomposition is in the solution of square systems of simultaneous
            linear equations.  This will fail if IsNonSingular() returns false
BRISC::GUI::MainMenuSimple menu to choose between the nodule viewer and the series viewer
BRISC::Core::MarkovRandomPerforms Markov random field operations on a LIDCNodule
BRISC::GUI::ProgramServes as a program entry point
BRISC::Matrix::QRDecompositionQR Decomposition. For an m-by-n matrix A with m >= n, the QR decomposition is an m-by-n orthogonal matrix Q and an n-by-n upper triangular matrix R so that A = Q*R
BRISC::GUI::SeriesViewerSimple DICOM series viewer for LIDC lung CT database
BRISC::Core::SimilarityContains static similarity measures for LIDC nodule comparison
BRISC::Matrix::SingularValueDecompositionSingular Value Decomposition
BRISC::GUI::SplashA "pretty" splash screen for displaying the status of a lenthy process
BRISC::Core::UnsafeBitmapProvides fast access to bitmap data via unsafe code
BRISC::Core::UnsafeBitmap::PixelDataStores 8-bit color RGB component data (24 bpp)
BRISC::Core::UtilContains miscellaneous static methods and constants, primarily dealing with DICOM file interaction

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