Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity inhomogeneity correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.

中文翻译：

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强度标准化简化了大脑 MR 图像分割。

通常，脑部 MR 图像在患者和扫描仪之间呈现显着的强度变化。因此，在一组图像上训练分类器并随后将其用于大脑分割可能会产生较差的结果。通常需要采用自适应迭代方法来考虑特定扫描的变化。这些方法复杂、难以实现并且通常涉及大量的计算成本。在本文中，提出了一种简单的非迭代方法用于脑 MR 图像分割。两种预处理技术，即强度不均匀性校正，更重要的是 MR 图像强度标准化，在分割之前使用，在使 MR 图像强度具有组织特定的数字意义方面起着至关重要的作用，这使我们能够进行非常简单的脑组织分割战略。首先利用基于矢量尺度的模糊连通性和某些形态学操作来生成脑颅内掩膜。然后估计每个脑组织的颅内掩模内每个体素的模糊隶属度值。最后，考虑到空间约束的最大似然标准被用于将颅内掩模中的所有体素分类为不同的脑组织组。一组不均匀性校正和强度标准化的图像被用作训练数据集。我们介绍了两种估计模糊隶属度值的方法。在第一种方法中，称为 SMG（基于高斯模型的简单成员资格），模糊隶属度值是通过将多元高斯模型拟合到每个脑组织的强度分布来估计的，其平均强度向量和协方差矩阵是从训练数据集中估计和固定的。第二种方法称为 SMH（基于直方图的简单隶属关系），直接通过从训练数据集中获得的每个脑组织的强度分布来估计模糊隶属度值。我们提出了几项研究，以基于 10 张正常受试者的临床 MR 图像和 10 张多发性硬化症 (MS) 患者的临床 MR 图像来评估这两种方法的性能。定量比较表明，这两种方法总体上比 k 最近邻 (kNN) 方法具有更好的准确性，并且比基于有限混合 (FM) 模型的期望最大化 (EM) 方法具有更好的效率。我们的方法和 EM 方法对于正常受试者数据集的准确性相似，但对于我们的患者数据集方法要好得多。