Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design

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    Abstract

    We consider a nonparametric, two-dimensional regression model that describes observations of Radon transformed images, i.e., an inverse regression model. Reconstructions from deterministic fan beam design by a certain kind of kernel-type estimators are considered and their asymptotic properties are investigated. The problem discussed is related to medical imaging procedures such as computerized tomography (CT).
    Original languageEnglish
    Title of host publication11th International Conference of Numerical Analysis and Applied Mathematics 2013
    Subtitle of host publicationICNAAM 2013
    EditorsTheodore Simos, George Psihoyios, Ch. Tsitouras
    PublisherAIP
    Pages728-731
    ISBN (Print)978-0-7354-1184-5
    DOIs
    Publication statusPublished - 2013
    Event11th International Conference of Numerical Analysis and Applied Mathematics 2013 - Rodos Palace Hotel, Rhodes, Greece
    Duration: 21 Sep 201327 Sep 2013
    Conference number: 11
    http://history.icnaam.org/icnaam_2013/index.htm

    Publication series

    NameAIP Conference Proceedings
    PublisherAIP
    Volume1558
    ISSN (Print)0094-243X

    Conference

    Conference11th International Conference of Numerical Analysis and Applied Mathematics 2013
    Abbreviated titleICNAAM 2013
    CountryGreece
    CityRhodes
    Period21/09/1327/09/13
    Internet address

    Keywords

    • Inverse problems
    • Multivariate regression
    • Nonparametric regression
    • Radon transform

    Cite this

    Proksch, K. (2013). Asymptotic Normality of Kernel Estimators for Images Observed under the Radon Transform in Fan Beam Design. In T. Simos, G. Psihoyios, & C. Tsitouras (Eds.), 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013 (pp. 728-731). (AIP Conference Proceedings; Vol. 1558). AIP. https://doi.org/10.1063/1.4825596