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Computer Regression

F. Graf, H.-P. Kriegel, M. Schubert, S. Poelsterl, A. CavallaroLudwig-Maximilians-Universit?¤t MunichDatabase Systems G......

数据结构 ? 17M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    F. Graf, H.-P. Kriegel, M. Schubert, S. Poelsterl, A. Cavallaro

    Ludwig-Maximilians-Universit?¤t Munich
    Database Systems Group
    Oettingenstra??e 67
    80538 Munich, Germany

    Data Set Information:

    The data was retrieved from a set of 53500 CT images from 74 different
    patients (43 male, 31 female).

    Each CT slice is described by two histograms in polar space.
    The first histogram describes the location of bone structures in the image,
    the second the location of air inclusions inside of the body.
    Both histograms are concatenated to form the final feature vector.
    Bins that are outside of the image are marked with the value -0.25.

    The class variable (relative location of an image on the axial axis) was
    constructed by manually annotating up to 10 different distinct landmarks in
    each CT Volume with known location. The location of slices in between
    landmarks was interpolated.

    Attribute Information:

    1. patientId:      Each ID identifies a different patient
    2. - 241.:         Histogram describing bone structures
    242. - 385.:       Histogram describing air inclusions
    386. reference:    Relative location of the image on the axial axis (class
     value). Values are in the range [0; 180] where 0 denotes
     the top of the head and 180 the soles of the feet.

    Relevant Papers:

    1. F. Graf, H.-P. Kriegel, M. Schubert, S. Poelsterl, A. Cavallaro
    2D Image Registration in CT Images using Radial Image Descriptors
    In Medical Image Computing and Computer-Assisted Intervention (MICCAI),
    Toronto, Canada, 2011.

    The data was used to predict the relative location of CT slices on
    the axial axis using k-nearest neighbor search.

    2. F. Graf, H.-P. Kriegel, S. P??lsterl, M. Schubert, A. Cavallaro
    Position Prediction in CT Volume Scans
    In Proceedings of the 28th International Conference on Machine
    Learning (ICML) Workshop on Learning for Global Challenges,
    Bellevue, Washington, WA, 2011.

    Here, the data was used to apply weighted combinations of image
    features for the localization of small sub volumes in CT scans.

    3. Cheng, Ming-Yen, and Hau-tieng Wu. "Local Linear Regression on Manifolds and its Geometric Interpretation." arXiv preprint  (2012).

    Citation Request:

    Please refer to the Machine Learning Repository's citation policy




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