BRISC::Matrix::CholeskyDecomposition | Cholesky 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::CombineCoOccurrence | Helper class for co-occurrence calculation |

BRISC::Matrix::EigenvalueDecomposition | Eigenvalues 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::FeatureVectorDialog | Feature vector selection dialog |

BRISC::Core::GaborFilter | Performs Gabor filtering operations on a LIDCNodule |

BRISC::Matrix::GeneralMatrix | .NET GeneralMatrix class |

BRISC::Core::GlobalCoOccurrence | Provides methods for calculating Haralick features on LIDCNodule objects |

BRISC::Core::LIDCImport | Converts LIDC data into .srs and .xml files for series and nodule viewers |

BRISC::Core::LIDCNodule | Represents a LIDC database nodule |

BRISC::Core::LIDCNodule::PhysicalSize | Structure for representing the physical size of a nodule |

BRISC::Core::LIDCNoduleDB | A collection of LIDCNodule objects |

BRISC::GUI::ListViewColumnSorter | Custom sorter class for nodule list views |

BRISC::Matrix::LUDecomposition | LU 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 < 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::MainMenu | Simple menu to choose between the nodule viewer and the series viewer |

BRISC::Core::MarkovRandom | Performs Markov random field operations on a LIDCNodule |

BRISC::GUI::Program | Serves as a program entry point |

BRISC::Matrix::QRDecomposition | QR 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::SeriesViewer | Simple DICOM series viewer for LIDC lung CT database |

BRISC::Core::Similarity | Contains static similarity measures for LIDC nodule comparison |

BRISC::Matrix::SingularValueDecomposition | Singular Value Decomposition |

BRISC::GUI::Splash | A "pretty" splash screen for displaying the status of a lenthy process |

BRISC::Core::UnsafeBitmap | Provides fast access to bitmap data via unsafe code |

BRISC::Core::UnsafeBitmap::PixelData | Stores 8-bit color RGB component data (24 bpp) |

BRISC::Core::Util | Contains miscellaneous static methods and constants, primarily dealing with DICOM file interaction |

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