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首页> 外文期刊>Medical image analysis >Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification.
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Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification.

机译：基于互信息的无监督MRI脑组织分类的自适应Markov建模。

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摘要

This paper presents a novel method for brain-tissue classification in magnetic resonance (MR) images that relies on a very general, adaptive statistical model of image neighborhoods. The method models MR-tissue intensities as derived from stationary random fields. It models the associated Markov statistics nonparametrically via a data-driven strategy. This paper describes the essential theoretical aspects underpinning adaptive, nonparametric Markov modeling and the theory behind the consistency of such a model. This general formulation enables the method to easily adapt to various kinds of MR images and the associated acquisition artifacts. It implicitly accounts for the intensity nonuniformity and performs reasonably well on T1-weighted MR data without nonuniformity correction. The method minimizes an information-theoretic metric on the probability density functions associated with image neighborhoods to produce an optimal classification. It automatically tunes its important internal parameters based on the information content of the data. Combined with an atlas-based initialization, it is completely automatic. Experiments on real, simulated, and multimodal data demonstrate the advantages of the method over the current state-of-the-art.
机译：本文提出了一种新的磁共振（MR）图像脑组织分类方法，该方法依赖于图像邻域的非常通用的自适应统计模型。该方法对从静止随机场得出的MR组织强度进行建模。它通过数据驱动策略以非参数方式对关联的Markov统计数据进行建模。本文介绍了适应性，非参数马尔可夫建模的基本理论方面以及该模型的一致性背后的理论。该一般公式使该方法能够轻松地适应各种MR图像和相关的采集伪像。它隐含地说明了强度不均匀性，并且在不进行不均匀性校正的情况下，对T1加权MR数据的性能相当好。该方法使关于与图像邻域相关联的概率密度函数的信息理论度量最小化，以产生最佳分类。它会根据数据的信息内容自动调整其重要的内部参数。结合基于图集的初始化，它是完全自动的。真实，模拟和多峰数据的实验证明了该方法相对于当前最新技术的优势。

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