Comput. Biol. Med. - Parameterization of the distribution of white and grey matter in MRI using the a-stable distribution.

Tópicos

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Resumo

This work presents a study of the distribution of the grey matter (GM) and white matter (WM) in brain magnetic resonance imaging (MRI). The distribution of GM and WM is characterized using a mixture of a-stable distributions. A Bayesian a-stable mixture model for histogram data is presented and unknown parameters are sampled using the Metropolis-Hastings algorithm. The proposed methodology is tested in 18 real images from the MRI brain segmentation repository. The GM and WM distributions are accurately estimated. The a-stable distribution mixture model presented in this paper can be used as previous step in more complex MRI segmentation procedures using spatial information. Furthermore, due to the fact that the a-stable distribution is a generalization of the Gaussian distribution, the proposed methodology can be applied instead of the Gaussian mixture model, which is widely used in segmentation of brain MRI in the literature.

Resumo Limpo

work present studi distribut grey matter gm white matter wm brain magnet reson imag mri distribut gm wm character use mixtur astabl distribut bayesian astabl mixtur model histogram data present unknown paramet sampl use metropolishast algorithm propos methodolog test real imag mri brain segment repositori gm wm distribut accur estim astabl distribut mixtur model present paper can use previous step complex mri segment procedur use spatial inform furthermor due fact astabl distribut general gaussian distribut propos methodolog can appli instead gaussian mixtur model wide use segment brain mri literatur

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