Moldovanu Simona, Toporaș Lenuta Pană, Biswas Anjan, Moraru Luminita
Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania.
The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania.
Entropy (Basel). 2020 Nov 14;22(11):1299. doi: 10.3390/e22111299.
A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (-values of 500 and 1250 s/mm) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the -value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.
提出了一种新的解决方案，以克服多模态医学受试者内图像配准的限制，该方案使用面向图像直方图梯度的互信息（MI）作为新的匹配标准。我们提出了一种基于线性变换和定向梯度的刚性多模态图像配准算法，用于将T2加权（T2w）图像（作为固定参考）与扩散张量成像（DTI）（500和1250 s/mm²的值）作为三名患者的浮动图像进行对齐，以补偿采集过程中的运动。扩散磁共振成像对运动非常敏感，尤其是当梯度脉冲的强度和持续时间（由²值表征）增加时。所提出的方法依赖于全脑表面，并将解剖特征的变异性纳入图像堆栈。稀疏特征是指使用哈里斯角点检测器算子检测到的角点，而密集特征则通过定向梯度图像直方图（HOG）使用所有图像像素，作为一对配准图像之间统计依赖程度的度量。作为密集特征的HOG聚焦于结构，并提取x和y方向的定向梯度图像。MI用作优化过程的目标函数。熵函数和联合熵函数使用HOG数据确定。为了确定最佳图像变换，使用基准配准误差（FRE）度量。我们将结果与使用源图像和目标图像中对应像素之间的统计强度关系计算的基于MI的强度结果进行比较。我们致力于全脑的方法与使用解剖特征或具有特定神经解剖学的感兴趣区域的配准算法相比，显示出更高的配准精度、鲁棒性和计算成本。尽管有额外的HOG计算任务，但基于MI的强度方法和基于MI的HOG方法的计算时间相当。
