n-SIFT: n-Dimensional Scale Invariant Feature Transform
n-SIFT: n-Dimensional Scale Invariant Feature Transform
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DOI：
10.1109/tip.2009.2024578
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发表时间：
2009-09-01
影响因子：
10.6
通讯作者：
Hamarneh, Ghassan
中科院分区：
文献类型：
--
作者：
Cheung, Warren;Hamarneh, Ghassan
我们提出了n维尺度不变特征变换（n-SIFT）方法提取和匹配显着特征的任意维数的标量图像，并比较该方法的性能与其他相关的功能。所提出的功能扩展的概念，用于2-D标量图像的计算机视觉SIFT技术提取和匹配独特的尺度不变特征。我们通过使用超球坐标的梯度和多维直方图来创建特征向量，将特征应用于任意维度的图像。我们分析了基于这些功能的全自动多模态医学图像匹配技术的性能，并成功地应用该技术来确定准确的特征点之间的对应关系对3-D MRI图像和动态3D +时间CT数据。
We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.