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21 March 2014 Spatially aware expectation maximization (SpAEM): application to prostate TRUS segmentation
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In this paper we introduce Spatially Aware Expectation Maximization (SpAEM), a new parameter estimation method which incorporates information pertaining to spatial prior probability into the traditional expectation- maximization framework. For estimating the parameters of a given class, the spatial prior probability allows us to weight the contribution of any pixel based on the probability of that pixel belonging to the class of interest. In this paper we evaluate SpAEM for the problem of prostate capsule segmentation in transrectal ultrasound (TRUS) images. In cohort of 6 patients, SpAEM qualitatively and quantitatively outperforms traditional EM in distinguishing the foreground (prostate) from background (non-prostate) regions by around 45% in terms of the Sorensen Dice overlap measure, when compared against expert annotations. The variance of the estimated parameters measured via Cramer-Rao Lower Bound suggests that SpAEM yields unbiased estimates. Finally, on a synthetic TRUS image, the Cramer-Von Mises (CVM) criteria shows that SpAEM improves the estimation accuracy by around 51% and 88% for prostate and background, respectively, as compared to traditional EM.
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Mahdi Orooji, Rachel Sparks, B. Nicolas Bloch, Ernest Feleppa, Dean Barratt, and Anant Madabhushi "Spatially aware expectation maximization (SpAEM): application to prostate TRUS segmentation", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90343Y (21 March 2014);

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