Department ofComputing Science, University of Alberta, Edmonton, AB, Canada.
IEEE Trans Biomed Eng. 2013 Apr;60(4):1069-79. doi: 10.1109/TBME.2012.2211017. Epub 2012 Aug 1.
Joint analysis of medical data collected from different imaging modalities has become a common clinical practice. Therefore, image fusion techniques, which provide an efficient way of combining and enhancing information, have drawn increasing attention from the medical community. In this paper, we propose a novel cross-scale fusion rule for multiscale-decomposition-based fusion of volumetric medical images taking into account both intrascale and interscale consistencies. An optimal set of coefficients from the multiscale representations of the source images is determined by effective exploitation of neighborhood information. An efficient color fusion scheme is also proposed. Experiments demonstrate that our fusion rule generates better results than existing rules.
联合分析来自不同成像模式的医学数据已经成为一种常见的临床实践。因此，图像融合技术提供了一种有效的信息组合和增强方式，引起了医学界越来越多的关注。在本文中，我们提出了一种新的跨尺度融合规则，用于基于多尺度分解的容积医学图像融合，同时考虑了尺度内和尺度间的一致性。通过有效利用邻域信息，从源图像的多尺度表示中确定了最优的一组系数。还提出了一种有效的彩色融合方案。实验表明，我们的融合规则比现有的规则产生了更好的结果。
