González Ballester M A, Zisserman A, Brady M
Department of Engineering Science, University of Oxford, UK.
Med Image Anal. 2000 Sep;4(3):189-200. doi: 10.1016/s1361-8415(00)00013-x.
The advent of new and improved imaging devices has allowed an impressive increase in the accuracy and precision of MRI acquisitions. However, the volumetric nature of the image formation process implies an inherent uncertainty, known as the partial volume effect, which can be further affected by artifacts such as magnetic inhomogeneities and noise. These degradations seriously challenge the application to MRI of any segmentation method, especially on data sets where the size of the object or effect to be studied is small relative to the voxel size, as is the case in multiple sclerosis and schizophrenia. We develop an approach to this problem by estimating a set of bounds on the spatial location of each organ to be segmented. First, we describe a method for 3D segmentation from voxel data which combines statistical classification and geometry-driven segmentation; then we discuss how the partial volume effect is estimated and object measurements are obtained. A comprehensive validation study and a set of results on clinical applications are also described.
新型且改良的成像设备的出现，使得磁共振成像（MRI）采集的准确性和精确性有了显著提高。然而，图像形成过程的体素性质意味着存在一种固有的不确定性，即部分容积效应，这种效应会受到诸如磁场不均匀性和噪声等伪影的进一步影响。这些退化严重挑战了任何分割方法在MRI中的应用，尤其是在待研究对象或效应的大小相对于体素大小较小的数据集上，如在多发性硬化症和精神分裂症的情况中。我们通过估计要分割的每个器官的空间位置的一组边界来解决这个问题。首先，我们描述一种从体素数据进行三维分割的方法，该方法结合了统计分类和几何驱动分割；然后我们讨论如何估计部分容积效应以及如何获得对象测量值。还描述了一项全面的验证研究以及一组临床应用结果。
