Additive Dirichlet models for projectional images

Simon Williams; Murk J. Bottema

doi:10.1117/12.911862

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24 February 2012 Additive Dirichlet models for projectional images

Simon Williams; Murk J. Bottema

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Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831443 (2012) https://doi.org/10.1117/12.911862
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

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Simon Williams and Murk J. Bottema "Additive Dirichlet models for projectional images", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831443 (24 February 2012); https://doi.org/10.1117/12.911862

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