A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.

中文翻译：

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用于分割磁共振图像的可扩展框架。


介绍了一种快速、准确、全自动的人脑磁共振图像分割方法。该方法具有良好的扩展性，可以快速分割高分辨率图像。该方法基于软聚类算法（模糊 c 均值）的修改，使其能够扩展到大型数据集。讨论了创建模糊 C 均值增量版本的两种类型的修改。对于中型到超大数据集，它们比模糊 c 均值要快得多，因为它们适用于数据的连续子集。它们在质量上与对所有数据应用模糊 C 均值相当。聚类算法与不均匀性校正和平滑相结合，用于创建自动分割人脑磁共振图像的框架。该框架适用于使用不同头部线圈、采集参数和场强从不同磁共振扫描仪采集的一组正常人脑体积。将结果与两个广泛使用的磁共振图像分割程序（统计参数映射和 FMRIB 软件库 (FSL)）的结果进行比较。结果与 FSL 相当，同时为更大数据量提供了显着的加速和更好的可扩展性。