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

[+] Author Affiliations
Lauren A. Christopher

Indiana University-Purdue University at Indianapolis, School of Engineering and Technology, Electrical and Computer Engineering Department, 723 W. Michigan Street, Indianapolis 46202, United States

Edward J. Delp

Purdue University, School of Electrical and Computer Engineering, West Lafayette, Indianapolis 47907, United States

J. Electron. Imaging. 23(4), 043018 (Aug 05, 2014). doi:10.1117/1.JEI.23.4.043018
History: Received December 27, 2013; Revised June 12, 2014; Accepted July 1, 2014
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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.

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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", J. Electron. Imaging. 23(4), 043018 (Aug 05, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.4.043018


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