Bresch Erik, Narayanan Shrikanth
Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
IEEE Trans Med Imaging. 2009 Mar;28(3):323-38. doi: 10.1109/TMI.2008.928920.
We describe a method for unsupervised region segmentation of an image using its spatial frequency domain representation. The algorithm was designed to process large sequences of real-time magnetic resonance (MR) images containing the 2-D midsagittal view of a human vocal tract airway. The segmentation algorithm uses an anatomically informed object model, whose fit to the observed image data is hierarchically optimized using a gradient descent procedure. The goal of the algorithm is to automatically extract the time-varying vocal tract outline and the position of the articulators to facilitate the study of the shaping of the vocal tract during speech production.
我们描述了一种使用图像的空间频域表示进行无监督区域分割的方法。该算法旨在处理包含人类声道气道二维正中矢状视图的大量实时磁共振（MR）图像序列。分割算法使用一个具有解剖学信息的对象模型，通过梯度下降过程对其与观测图像数据的拟合进行分层优化。该算法的目标是自动提取随时间变化的声道轮廓和发音器官的位置，以便于研究语音产生过程中声道的塑形。
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