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Proceedings Paper

Additive Dirichlet models for projectional images
Author(s): Simon Williams; Murk J. Bottema
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Paper 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.

Paper Details

Date Published: 24 February 2012
PDF: 6 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831443 (24 February 2012); doi: 10.1117/12.911862
Show Author Affiliations
Simon Williams, Flinders Univ. (Australia)
Murk J. Bottema, Flinders Univ. (Australia)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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