Comparing three-dimensional Bayesian segmentations for images with low signal-to-noise ratio (SNR<1) and strong attenuation

Lauren A. Christopher; Edward J. Delp

doi:10.1117/1.JEI.23.4.043018

5 August 2014 Comparing three-dimensional Bayesian segmentations for images with low signal-to-noise ratio (SNR<1) and strong attenuation

Lauren A. Christopher, Edward J. Delp

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 4, 043018 (August 2014). https://doi.org/10.1117/1.JEI.23.4.043018

Abstract

This paper examines three Bayesian statistical segmentation techniques with an innovative attenuation compensation on synthetic data and breast ultrasound medical images. All use expectation maximization for estimating the Gaussian model parameters and segment the data using a three-dimensional (3-D) Markov random field pixel neighborhood. This paper compares three Bayesian segmentation techniques: maximum a posteriori simulated annealing (MAP-SA), MAP iterated conditional modes (MAP-ICM), and maximization of posterior marginals (MPM). We conclude that because of the high speckle noise and adverse attenuation challenges of breast ultrasound, the MPM algorithm has the best performance. This is due to better localized segmentation than the other MAP techniques. We present results first with synthetic images then with breast ultrasound. Our new contributions for a 3-D breast ultrasound produce improved results using a model of the noise, in which the Gaussian mean is proportional to the image attenuation with depth, combined with a new prior probability model.

Citation Download Citation

Lauren A. Christopher and Edward J. Delp "Comparing three-dimensional Bayesian segmentations for images with low signal-to-noise ratio (SNR<1) and strong attenuation," Journal of Electronic Imaging 23(4), 043018 (5 August 2014). https://doi.org/10.1117/1.JEI.23.4.043018

Published: 5 August 2014

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