School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Comput Biol Med. 2013 Oct;43(10):1484-96. doi: 10.1016/j.compbiomed.2013.07.017. Epub 2013 Aug 2.
In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen-Tsallis.
在本文中，我们提出了一种新的基于强度的医学图像配准相似性度量方法。传统的基于强度的方法对强度变形、造影剂和噪声很敏感。虽然残差复杂度在某些情况下可以解决这个问题，但是参数的相对修改会产生截然不同的结果。通过在残差复杂度的残差项中引入一个专门设计的指数加权函数，所提出的相似性度量方法由于自动对参考图像和变形浮动图像之间的残差图像进行加权，因此表现良好。我们利用参考图像的局部方差来对指数加权函数进行建模。所提出的技术应用于脑磁共振图像、乳腺动态对比增强磁共振图像（DCE-MRI）和增强 3D CT 肝脏图像。实验结果清楚地表明，与互信息、残差复杂度和 Jensen-Tsallis 相比，所提出的方法具有更准确和鲁棒的性能。
