Pitiot Alain, Totman John, Gowland Penny
Brain & Body Centre, University of Nottingham, UK.
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):759-66. doi: 10.1007/978-3-540-75757-3_92.
Automatic classification of neurological tissues is a first step to many structural analysis pipelines. Most computational approaches are designed to extract the best possible classification results out of MR data acquired with standard clinical protocols. We observe that the characteristics of the latter owe more to the historical circumstances under which they were developed and the visual appreciation of the radiographer who acquires the images than to the optimality with which they can be classified with an automatic algorithm. We submit that better performances could be obtained by considering the acquisition and analysis processes conjointly rather than optimising them independently. Here, we propose such a joint approach to MR tissue classification in the form of a fast MR sequence, which nulls the magnitude and changes the sign of the phase at the boundary between tissue types. A simple phase-based thresholding algorithm then suffices to segment the tissues. Preliminary results show promises to simplify and shorten the overall classification process.
神经组织的自动分类是许多结构分析流程的第一步。大多数计算方法旨在从采用标准临床方案采集的磁共振（MR）数据中提取尽可能好的分类结果。我们观察到，后者的特性更多地归因于其开发时的历史情况以及获取图像的放射技师的视觉判断，而非自动算法对其进行分类的最优性。我们认为，通过联合考虑采集和分析过程而非独立优化它们，可以获得更好的性能。在此，我们以一种快速MR序列的形式提出这样一种用于MR组织分类的联合方法，该序列使幅度归零并改变组织类型边界处相位的符号。然后，一种简单的基于相位的阈值算法就足以对组织进行分割。初步结果显示有望简化和缩短整个分类过程。
