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- W68381469 abstract "The paper proposes a novel Magnetic Resonance Image(MRI) segmentation method based on Expectation-Maximization and Bayesian Information Criterion. The Expectation-Maximization theory is used to estimate the data distribution of the MRI firstly. The number of class is calculated by Bayesian Information Criterion. The Maximum Likelihood is employed to classify the image pixels into the nearest class. The excellence of the proposed method is being independent to original estimate and it can be used in unsupervised image segmentation. The real MR images are used in the experiment. The results show that this method is efficient." @default.
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- W68381469 date "2012-01-01" @default.
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- W68381469 title "Unsupervised Brain MRI Segmentation Using Improved Expectation-Maximization Algorithm" @default.
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- W68381469 doi "https://doi.org/10.1007/978-3-642-29637-6_106" @default.
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