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A Bayesian Bounded Asymmetric Mixture Model With Segmentation Application
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DOI：10.1109/JBHI.2013.2264749.png)
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摘要
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Segmentation of a medical image based on the modeling and estimation of the tissue intensity probability density functions via a Gaussian mixture model has recently received great attention. However, the Gaussian distribution is unbounded and symmetrical around its mean. This study presents a new bounded asymmetric mixture model for analyzing both univariate and multivariate data. The advantage of the proposed model is that it has the flexibility to fit different shapes of observed data such as non-Gaussian, nonsymmetric, and bounded support data. Another advantage is that each component of the proposed model has the ability to model the observed data with different bounded support regions, which is suitable for application on image segmentation. Ourmethod is intuitively appealing, simple, and easy to implement. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood function. Numerical experiments are presented where the proposed model is tested in various images from simulated to real 3-D medical ones.
Keyword:
Bayesian estimation
bounded support regions
medical image segmentation
negative log-likelihood function
non-Gaussian
nonsymmetric
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