53500张CT图像中获取的患者数据集（43名男性31名女性）

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