Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576.
IEEE Trans Biomed Eng. 2011 Apr;58(4):991-1000. doi: 10.1109/TBME.2010.2093576. Epub 2010 Nov 22.
In this paper, we propose a Markov random field-based method that uses saliency and gradient information for elastic registration of dynamic contrast enhanced (DCE) magnetic resonance (MR) images of the heart. DCE-MR images are characterized by rapid intensity changes over time, thus posing challenges for conventional intensity-based registration methods. Saliency information contributes to a contrast invariant metric to identify similar regions in spite of contrast enhancement. Its robustness and accuracy are attributed to a close adherence to a neurobiological model of the human visual system (HVS). The HVS has a remarkable ability to match images in the face of intensity changes and noise. This ability motivated us to explore the efficacy of such a model for registering DCE-MR images. The data penalty is a combination of saliency and gradient information. The smoothness cost depends upon the relative displacement and saliency difference of neighboring pixels. Saliency is also used in a modified narrow band graph cut framework to identify relevant pixels for registration, thus reducing the number of graph nodes and computation time. Experimental results on real patient images demonstrate superior registration accuracy for a combination of saliency and gradient information over other similarity metrics.
在本文中，我们提出了一种基于马尔可夫随机场的方法，该方法利用显著度和梯度信息对心脏的动态对比增强（DCE）磁共振（MR）图像进行弹性配准。DCE-MR 图像的特点是随时间快速变化的强度，因此对传统的基于强度的配准方法提出了挑战。显著度信息有助于提供一种对比不变的度量标准，以识别增强后的相似区域。其鲁棒性和准确性归因于其紧密遵循人类视觉系统（HVS）的神经生物学模型。HVS 具有在面对强度变化和噪声时匹配图像的出色能力。这种能力促使我们探索这种模型用于注册 DCE-MR 图像的效果。数据惩罚是显著度和梯度信息的组合。平滑性成本取决于相邻像素的相对位移和显著度差异。在改进的窄带图割框架中，还使用了显著度来识别用于注册的相关像素，从而减少了图节点的数量和计算时间。对真实患者图像的实验结果表明，显著度和梯度信息的组合比其他相似性度量标准具有更高的配准精度。
