van den Elsen P A, Maintz J B, Viergever M A
Computer Vision Research Group, University Hospital Utrecht, The Netherlands.
Brain Topogr. 1992 Winter;5(2):153-7. doi: 10.1007/BF01129043.
Clinical diagnosis, as well as therapy planning and evaluation, are increasingly supported by multimodal images. There are many instances desiring integration of the information obtained by various imaging devices. This paper describes a new approach to match images of different modalities. Differential operators are used in combination with Gaussian blurring to extract geometric features from the images that correspond to similar structures. The resulting 'feature' images may be used with existing matching techniques that minimize the distance between the features in the images to be matched. Our first application of this new approach concerns matching of MRI and CT brain images. The so-called L upsilon upsilon operator produces a ridge-like feature image from which in CT and MRI the center curve of the cranium is easily extracted. First results of this operator's performance in matching tasks are shown. Another promising operator is the 'umbilicity' operator, which is presented in combination with SPECT images.
临床诊断以及治疗方案的规划与评估越来越多地得到多模态图像的支持。在许多情况下，都需要整合通过各种成像设备获取的信息。本文描述了一种匹配不同模态图像的新方法。微分算子与高斯模糊相结合，用于从对应于相似结构的图像中提取几何特征。所得的“特征”图像可与现有的匹配技术一起使用，这些技术可使待匹配图像中的特征之间的距离最小化。我们对这种新方法的首次应用涉及磁共振成像（MRI）和计算机断层扫描（CT）脑部图像的匹配。所谓的拉普拉斯算子会生成一个脊状特征图像，从该图像中可以轻松提取CT和MRI中颅骨的中心曲线。展示了该算子在匹配任务中的初步性能结果。另一个有前景的算子是“脐点”算子，它与单光子发射计算机断层扫描（SPECT）图像结合呈现。
