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Erschienen in:
A Gaussian Mixture Model Approach to Classifying Response Types Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

13. Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models

verfasst von : Wenmin Chen, Wentao Fan, Nizar Bouguila, Bineng Zhong

Erschienen in: Mixture Models and Applications

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Color Image Segmentation Using Semi-bounded Finite Mixture Models by Incorporating Mean Templates
Nächstes Kapitel Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation
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Metadaten
Titel
Medical Image Segmentation Based on Spatially Constrained Inverted Beta-Liouville Mixture Models
verfasst von
Wenmin Chen
Wentao Fan
Nizar Bouguila
Bineng Zhong
Copyright-Jahr
2020
Buch
Mixture Models and Applications

Print ISBN: 978-3-030-23875-9

Electronic ISBN: 978-3-030-23876-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-23876-6_13

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