Hellier Pierre, Barillot Christian
IRISA, INRIA-CNRS, Campus de Beaulieu, 35042 Rennes Cedex, France.
Comput Methods Programs Biomed. 2004 Aug;75(2):107-15. doi: 10.1016/j.cmpb.2004.01.002.
Image fusion is of utmost importance for many applications in image analysis. Particularly in medical imaging, images of different modalities are necessary because they provide complementary information that must be merged for an optimal use. The fusion of these images, which can be achieved through a registration process, makes it possible to superimpose all available information on the same frame. In many cases, a rigid transformation is sufficient to align correctly the images. However, there are cases where a non-rigid transformation is needed: geometrical distortions present in one image, non-rigid motion, etc. The purpose of this paper is to propose a generic method to account for these deformations in case of multimodal images. We have applied the algorithm in the particular context of 3D medical images and present results on simulated and real data.
图像融合对于图像分析中的许多应用至关重要。特别是在医学成像中,不同模态的图像是必要的,因为它们提供了互补信息,必须将这些信息合并才能实现最佳使用。这些图像的融合可以通过配准过程来实现,从而能够将所有可用信息叠加在同一帧上。在许多情况下,刚性变换足以正确对齐图像。然而,在某些情况下需要非刚性变换:例如一幅图像中存在的几何失真、非刚性运动等。本文的目的是提出一种通用方法,用于处理多模态图像中的这些变形。我们已将该算法应用于3D医学图像的特定背景下,并展示了在模拟数据和真实数据上的结果。