基于纹理滤波的无监督配准方法及其在肝脏电子计算机断层扫描中的应用

Texture filtering based unsupervised registration methods and its application in liver computed tomography images

图像配准在肝脏疾病的计算机辅助诊断和手术规划方面具有重要的临床意义。基于深度学习的配准方法使得肝脏电子计算机断层扫描(CT)图像配准具有较高的实时性和准确度。然而,现有方法在配准具有大位移和大形变的图像时,存在配准后图像的纹理信息发生改变的问题,因而难以将其应用在后续的图像处理与临床诊断中。基于此,本文提出一种新颖的基于纹理滤波的无监督配准方法,实现了肝脏CT图像的配准。该方法首先基于L0梯度最小化的纹理滤波算法消除CT图像中肝脏表面的纹理信息,使得配准过程仅参考两幅图像的空间结构信息进行配准,从而解决纹理改变的问题。然后,基于级联网络配准具有大位移和大形变的图像,循序渐进地将待配准图像与参考图像在空间结构上对齐。此外本文还提出一种新的衡量指标——直方图相关系数,以衡量配准后纹理改变的程度。实验结果表明,本文所提出的方法具有较高的配准精度,有效地改善了级联网络中存在纹理改变的问题,并且提升了在空间结构对应和抗折叠性能两方面的配准效果。因此,本文所提方法或有助于提升医学图像配准的科学性,促进医学图像配准安全可靠地应用在肝脏疾病的计算机辅助诊断和手术规划方面。

Image registration is of great clinical importance in computer aided diagnosis and surgical planning of liver diseases. Deep learning-based registration methods endow liver computed tomography(CT) image registration with characteristics of real-time and high accuracy. However, existing methods in registering images with large displacement and deformation are faced with the challenge of the texture information variation of the registered image, resulting in subsequent erroneous image processing and clinical diagnosis. To this end, a novel unsupervised registration method based on the texture filtering is proposed in this paper to realize liver CT image registration. Firstly, the texture filtering algorithm based on L0 gradient minimization eliminates the texture information of liver surface in CT images, so that the registration process can only refer to the spatial structure information of two images for registration, thus solving the problem of texture variation. Then, we adopt the cascaded network to register images with large displacement and large deformation, and progressively align the fixed image with the moving one in the spatial structure. In addition, a new registration metric, the histogram correlation coefficient, is proposed to measure the degree of texture variation after registration. Experimental results show that our proposed method achieves high registration accuracy, effectively solves the problem of texture variation in the cascaded network, and improves the registration performance in terms of spatial structure correspondence and anti-folding capability. Therefore, our method helps to improve the performance of medical image registration, and make the registration safely and reliably applied in the computer-aided diagnosis and surgical planning of liver diseases.