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Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple-Sclerosis

机译：多发性硬化症患者脑部MRI的组织强度最佳高斯混合模型

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Brain tissue segmentation is important in studying markers in human brain Magnetic Resonance Images (MRI) of patients with diseases such as Multiple Sclerosis (MS). Parametric segmentation approaches typically assume unimodal Gaussian distributions on MRI intensities of individual tissue classes, even in applications on multi-spectral images. However, this assumption has not been rigorously verified especially in the context of MS. In this work, we evaluate the local MRI intensities of both healthy and diseased brain tissues of 21 multi-spectral MRIs (63 volumes in total) of MS patients for adherence to this assumption. We show that the tissue intensities are not uniform across the brain and vary across (anatomical) regions of the brain. Consequently, we show that Gaussian mixtures can better model the multi-spectral intensities. We utilize an Expectation Maximization (EM) based approach to learn the models along with a symmetric Jeffreys divergence criterion to study differences in intensity distributions. The effects of these findings are also empirically verified on automatic segmentation of brains with MS.

机译：脑组织分割对于研究患有多发性硬化症（MS）等疾病的人脑磁共振图像（MRI）中的标记非常重要。参数分割方法通常假设单个组织类别的MRI强度具有单峰高斯分布，即使在多光谱图像上也是如此。但是，此假设尚未得到严格验证，尤其是在MS的情况下。在这项工作中，我们评估了21例多光谱MRI（总计63册）的MS患者的健康和患病脑组织的局部MRI强度，以符合该假设。我们显示，整个大脑的组织强度不均匀，并且在大脑的（解剖）区域之间变化。因此，我们表明高斯混合可以更好地模拟多光谱强度。我们利用基于期望最大化（EM）的方法来学习模型，并使用对称Jeffreys发散准则来研究强度分布的差异。这些发现的效果也可以通过MS自动分割大脑进行经验验证。

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《Machine learning in medical imaging》|2010年|p.165-173|共9页
会议地点 Beijing(CN);Beijing(CN);Beijing(CN);Beijing(CN)
作者
Yiming Xiao; Mohak Shah; Simon Francis; Douglas L. Arnold; Tal Arbel; D. Louis Collins;
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作者单位

Montreal Neurological Institute, McGill University, Canada;

Centre for Intelligent Machines, McGill University, Canada,NeuroRx Research, Montreal, Canada;

Montreal Neurological Institute, McGill University, Canada,NeuroRx Research, Montreal, Canada;

Montreal Neurological Institute, McGill University, Canada,NeuroRx Research, Montreal, Canada;

Centre for Intelligent Machines, McGill University, Canada;

Montreal Neurological Institute, McGill University, Canada;

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正文语种 eng
中图分类医用物理学;
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1. Brain MRI image segmentation based on learning local variational Gaussian mixture models [J] . Xia Yong, Ji Zexuan, Zhang Yanning Neurocomputing . 2016,第sepa5期

机译：基于学习局部变分高斯混合模型的脑MRI图像分割
2. Gaussian Mixture Models for Brain Activation Detection from fMRI Data [J] . Gaurav Garg, Girijesh Prasad, Lalit Garg, International Journal of Bioelectromagnetism . 2011,第4期

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3. Quantification of brown and white adipose tissue based on Gaussian mixture model using water–fat and T 2 * T 2 * T 2 * T T 2 2 * * MRI in adolescents [J] . Hui Steve C.N., Ko Jacky K.L., Zhang Teng, Journal of magnetic resonance imaging: JMRI . 2017,第3期

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4. Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple- Sclerosis [C] . Yiming Xiao, Mohak Shah, Simon Francis, International Workshop on Machine Learning in Medical Imaging . 2010

机译：多发性硬化症患者脑部MRI组织强度的最佳高斯混合模型
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机译：利用MRI和计算建模的非侵入性评估脑脊液和脑组织生物力学
6. Data on MRI brain lesion segmentation using K-means and Gaussian Mixture Model-Expectation Maximization [O] . Ju Qiao, Xuezhu Cai, Qian Xiao, 2019

机译：使用K均值和高斯混合模型期望的MRI脑病变分割数据
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Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple-Sclerosis

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