RISM: Single-Modal Image Registration via Rank-Induced Similarity Measure
RISM: Single-Modal Image Registration via Rank-Induced Similarity Measure
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DOI：
10.1109/tip.2015.2479462
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发表时间：
2015-12-01
影响因子：
10.6
通讯作者：
Fatemizadeh, Emad
中科院分区：
文献类型：
--
作者：
Ghaffari, Aboozar;Fatemizadeh, Emad
相似性度量是图像配准中的一个重要环节。大多数传统的基于强度的相似性度量(例如，平方差和、相关系数和互信息)假定图像是静止的，并且逐像素独立。这些相似性度量忽略了像素强度之间的相关性；因此，不能实现完美的图像配准，特别是在存在空间变化的强度失真的情况下。这里，我们假设空间变化的强度失真(如偏置场)是一个低阶矩阵。基于这一假设，我们将图像配准问题描述为一个非线性低阶矩阵分解(NLLRMD)。因此，同时实现了图像配准和空间变化强度失真的校正。我们说明了NLLRMD的唯一性，并由此提出了在存在空间变化的强度失真的情况下，差值图像的秩次作为稳健的相似性。最后，通过引入高斯噪声，引入了基于差值图像奇异值的秩导出相似性度量。在本文所研究的模拟和真实问题上，这种方法都产生了临床可接受的配准结果，并且优于其他最先进的方法，例如剩余复杂性方法。
Similarity measure is an important block in image registration. Most traditional intensity-based similarity measures (e.g., sum-of-squared-difference, correlation coefficient, and mutual information) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation between pixel intensities; hence, perfect image registration cannot be achieved, especially in the presence of spatially varying intensity distortions. Here, we assume that spatially varying intensity distortion (such as bias field) is a low-rank matrix. Based on this assumption, we formulate the image registration problem as a nonlinear and low-rank matrix decomposition (NLLRMD). Therefore, image registration and correction of spatially varying intensity distortion are simultaneously achieved. We illustrate the uniqueness of NLLRMD, and therefore, we propose the rank of difference image as a robust similarity in the presence of spatially varying intensity distortion. Finally, by incorporating the Gaussian noise, we introduce rank-induced similarity measure based on the singular values of the difference image. This measure produces clinically acceptable registration results on both simulated and real-world problems examined in this paper, and outperforms other state-of-the-art measures such as the residual complexity approach.