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Medical image segmentation based on spatially constrained inverted beta-Liouville mixture models. (English) Zbl 1434.62114

Bouguila, Nizar (ed.) et al., Mixture models and applications. Cham: Springer. Unsuperv. Semi-Superv. Learn., 307-324 (2020).
Summary: In this chapter, we propose an image segmentation method based on a spatially constrained inverted Beta-Liouville (IBL) mixture model for segmenting medical images. Our method adopts the IBL distribution as the basic distribution, which can demonstrate better performance than commonly used distributions (such as Gaussian distribution) in image segmentation. To improve the robustness of our image segmentation method against noise, the spatial relationship among nearby pixels is imposed into our model by using generalized means. We develop a variational Bayes inference algorithm to learn the proposed model, such that model parameters can be efficiently estimated in closed form. In our experiments, we use both simulated and real brain magnetic resonance imaging (MRI) data to validate our model.
For the entire collection see [Zbl 1430.62012].

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62P10 Applications of statistics to biology and medical sciences; meta analysis
62H35 Image analysis in multivariate analysis

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References:

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