Priebe Carey E, Miller Michael I, Ratnanather J Tilak
Center for Imaging Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Comput Stat Data Anal. 2006 Jan;50(2):551-567. doi: 10.1016/j.csda.2004.09.003.
We present a statistically innovative as well as scientifically and practically relevant method for automatically segmenting magnetic resonance images using hierarchical mixture models. Our method is a general tool for automated cortical analysis which promises to contribute substantially to the science of neuropsychiatry. We demonstrate that our method has advantages over competing approaches on a magnetic resonance brain imagery segmentation task.
我们提出了一种利用分层混合模型自动分割磁共振图像的统计创新方法，该方法在科学和实践方面均具有相关性。我们的方法是一种用于自动皮质分析的通用工具，有望为神经精神病学的科学发展做出重大贡献。我们证明，在磁共振脑图像分割任务中，我们的方法比其他竞争方法具有优势。
