Marsland Stephen, Twining Carole J
Institute of Information Sciences, Massey University, Private Bag 11222, Palmerston North, New Zealand.
IEEE Trans Med Imaging. 2004 Aug;23(8):1006-20. doi: 10.1109/TMI.2004.831228.
Groupwise nonrigid registrations of medical images define dense correspondences across a set of images, defined by a continuous deformation field that relates each target image in the group to some reference image. These registrations can be automatic, or based on the interpolation of a set of user-defined landmarks, but in both cases, quantifying the normal and abnormal structural variation across the group of imaged structures implies analysis of the set of deformation fields. We contend that the choice of representation of the deformation fields is an integral part of this analysis. This paper presents methods for constructing a general class of multi-dimensional diffeomorphic representations of deformations. We demonstrate, for the particular case of the polyharmonic clamped-plate splines, that these representations are suitable for the description of deformations of medical images in both two and three dimensions, using a set of two-dimensional annotated MRI brain slices and a set of three-dimensional segmented hippocampi with optimized correspondences. The class of diffeomorphic representations also defines a non-Euclidean metric on the space of patterns, and, for the case of compactly supported deformations, on the corresponding diffeomorphism group. In an experimental study, we show that this non-Euclidean metric is superior to the usual ad hoc Euclidean metrics in that it enables more accurate classification of legal and illegal variations.
医学图像的逐组非刚性配准定义了一组图像之间的密集对应关系,由一个连续变形场定义,该变形场将组中的每个目标图像与某个参考图像相关联。这些配准可以是自动的,也可以基于一组用户定义地标的插值,但在这两种情况下,量化成像结构组中的正常和异常结构变化都意味着对变形场集进行分析。我们认为变形场表示的选择是此分析的一个组成部分。本文提出了构建一类通用的多维微分同胚变形表示的方法。对于多调和夹支板样条的特殊情况,我们使用一组二维标注的MRI脑切片和一组具有优化对应关系的三维分割海马体,证明这些表示适用于描述二维和三维医学图像的变形。微分同胚表示类还在模式空间上定义了一个非欧几里得度量,对于紧支变形的情况,在相应的微分同胚群上定义了一个非欧几里得度量。在一项实验研究中,我们表明这种非欧几里得度量优于通常的临时欧几里得度量,因为它能够更准确地对合法和非法变化进行分类。