Additive Dirichlet models for projectional images

Simon Williams, Murk Bottema

    Research output: Contribution to conferencePaper

    Abstract

    An important difference between projection images such as x-rays and natural images is that the intensity at a single pixel in a projection image comprises information from all objects between the source and detector. In order to exploit this information, a Dirichlet mixture of Gaussian distributions is used to model the intensity function forming the projection image. The model requires initial seeding of Gaussians and uses the EM (estimation maximisation) algorithm to arrive at a final model. The resulting models are shown to be robust with respect to the number and positions of the Gaussians used to seed the algorithm. As an example, a screening mammogram is modelled as the Dirichlet sum of Gaussians suggesting possible application to early detection of breast cancer.

    Original languageEnglish
    DOIs
    Publication statusPublished - 14 May 2012
    EventMedical Imaging 2012: Image Processing -
    Duration: 1 Jan 2012 → …

    Conference

    ConferenceMedical Imaging 2012: Image Processing
    Period1/01/12 → …

    Keywords

    • Additive Gaussian mixture model
    • Dirichlet distribution
    • Mammography
    • X-ray imaging

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  • Cite this

    Williams, S., & Bottema, M. (2012). Additive Dirichlet models for projectional images. Paper presented at Medical Imaging 2012: Image Processing, . https://doi.org/10.1117/12.911862