5 August 2014 Comparing three-dimensional Bayesian segmentations for images with low signal-to-noise ratio (SNR<1) and strong attenuation
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J. of Electronic Imaging, 23(4), 043018 (2014). doi: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.
© 2014 SPIE and IS&T
Lauren A. Christopher, 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
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