Spatially aware expectation maximization (SpAEM): application to prostate TRUS segmentation

Mahdi Orooji; Rachel Sparks; B. Nicolas Bloch; Ernest Feleppa; Dean Barratt; Anant Madabhushi

doi:10.1117/12.2043981

21 March 2014 Spatially aware expectation maximization (SpAEM): application to prostate TRUS segmentation

Mahdi Orooji, Rachel Sparks, B. Nicolas Bloch, Ernest Feleppa, Dean Barratt, Anant Madabhushi

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90343Y (2014) https://doi.org/10.1117/12.2043981
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

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.

Citation Download Citation

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); https://doi.org/10.1117/12.2043981

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available