Vemuri B C, Ye J, Chen Y, Leonard C M
Department of CISE, University of Florida, Gainesville, FL 32611, USA.
Med Image Anal. 2003 Mar;7(1):1-20. doi: 10.1016/s1361-8415(02)00063-4.
Image registration is an often encountered problem in various fields including medical imaging, computer vision and image processing. Numerous algorithms for registering image data have been reported in these areas. In this paper, we present a novel curve evolution approach expressed in a level-set framework to achieve image intensity morphing and a simple non-linear PDE for the corresponding coordinate registration. The key features of the intensity morphing model are that (a) it is very fast and (b) existence and uniqueness of the solution for the evolution model are established in a Sobolev space as opposed to using viscosity methods. The salient features of the coordinate registration model are its simplicity and computational efficiency. The intensity morph is easily achieved via evolving level-sets of one image into the level-sets of the other. To explicitly estimate the coordinate transformation between the images, we derive a non-linear PDE-based motion model which can be solved very efficiently. We demonstrate the performance of our algorithm on a variety of images including synthetic and real data. As an application of the PDE-based motion model, atlas based segmentation of hippocampal shape from several MR brain scans is depicted. In each of these experiments, automated hippocampal shape recovery results are validated via manual "expert" segmentations.
图像配准是医学成像、计算机视觉和图像处理等各个领域经常遇到的问题。在这些领域已经报道了许多用于配准图像数据的算法。在本文中，我们提出了一种在水平集框架中表示的新颖曲线演化方法，以实现图像强度变形，并提出了一个用于相应坐标配准的简单非线性偏微分方程。强度变形模型的关键特性是：（a）它速度非常快；（b）与使用粘性方法不同，演化模型解的存在性和唯一性是在索伯列夫空间中建立的。坐标配准模型的显著特点是其简单性和计算效率。通过将一幅图像的水平集演化成另一幅图像的水平集，可以轻松实现强度变形。为了明确估计图像之间的坐标变换，我们推导了一个基于非线性偏微分方程的运动模型，该模型可以非常有效地求解。我们在包括合成数据和真实数据在内的各种图像上展示了我们算法的性能。作为基于偏微分方程的运动模型的一个应用，描述了从多个脑部磁共振扫描中基于图谱的海马形状分割。在每个实验中，通过手动“专家”分割来验证自动海马形状恢复结果。
