Evaluating intensity normalization on MRIs of human brain with multiple sclerosis
Evaluating intensity normalization on MRIs of human brain with multiple sclerosis
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DOI：
10.1016/j.media.2010.12.003
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发表时间：
2011-04-01
影响因子：
10.9
通讯作者：
Arbel, Tal
中科院分区：
文献类型：
--
作者：
Shah, Mohak;Xiao, Yiming;Arbel, Tal
强度归一化是人脑磁共振图像研究和分析中的一个重要的预处理步骤。由于大多数参数监督的自动图像分割和分类方法都是基于标准化的灰度范围来假设灰度分布，因此灰度归一化起着非常重要的作用。Nyul和他的同事提出了一种快速而准确的强度归一化方法。在这项工作中，我们首次提出了这种方法在实际临床领域的广泛验证，即使在考虑到扫描仪特定伪影的强度不均匀性校正之后，MRI体积也会受到变化的影响，例如多部位多扫描仪采集导致的数据异质性。多发性硬化症(MS)病变的存在和大脑中疾病进展的阶段。利用分布发散准则，在分割方法的分布假设下，我们评估了归一化在绘制相同组织类型的更均匀的强度时的有效性，同时导致更好的组织类型分离。与标准贝叶斯分类器、基于孤立点检测的方法和基于马尔可夫随机场(MRF)的贝叶斯分类器三种图像分割算法相比，我们还证明了基于十进制分段线性方法在MS病变分割任务中的优势。最后，为了证明归一化效果与分割算法的复杂性的独立性，我们对Nyul方法和线性归一化方法进行了比较，这些算法的复杂性不断增加，包括一个具有最大似然参数估计的标准贝叶斯分类器和一个集成了数据先验的贝叶斯分类器，此外，我们还使用基于马尔可夫随机场的后处理来平滑后验数据的贝叶斯分类器。在所有相关的情况下，使用显著性检验来验证观察结果的统计相关性。(C)2010爱思唯尔B.V.保留所有权利。
Intensity normalization is an important pre-processing step in the study and analysis of Magnetic Resonance Images (MRI) of human brains. As most parametric supervised automatic image segmentation and classification methods base their assumptions regarding the intensity distributions on a standardized intensity range, intensity normalization takes on a very significant role. One of the fast and accurate approaches proposed for intensity normalization is that of Nyul and colleagues. In this work, we present, for the first time, an extensive validation of this approach in real clinical domain where even after intensity inhomogeneity correction that accounts for scanner-specific artifacts, the MRI volumes can be affected from variations such as data heterogeneity resulting from multi-site multi-scanner acquisitions. the presence of multiple sclerosis (MS) lesions and the stage of disease progression in the brain. Using the distributional divergence criteria, we evaluate the effectiveness of the normalization in rendering, under the distributional assumptions of segmentation approaches, intensities that are more homogenous for the same tissue type while simultaneously resulting in better tissue type separation. We also demonstrate the advantage of the decile based piece-wise linear approach on the task of MS lesion segmentation against a linear normalization approach over three image segmentation algorithms: a standard Bayesian classifier, an outlier detection based approach and a Bayesian classifier with Markov Random Field (MRF) based post-processing. Finally, to demonstrate the independence of the effectiveness of normalization from the complexity of segmentation algorithm, we evaluate the Nyul method against the linear normalization on Bayesian algorithms of increasing complexity including a standard Bayesian classifier with Maximum Likelihood parameter estimation and a Bayesian classifier with integrated data priors, in addition to the above Bayesian classifier with MRF based post-processing to smooth the posteriors. In all relevant cases, the observed results are verified for statistical relevance using significance tests. (C) 2010 Elsevier B.V. All rights reserved.