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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor
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Non-Rigid Multi-Modal 3D Medical Image Registration Based on Foveated Modality Independent Neighborhood Descriptor

机译：基于凹模态独立邻域描述符的非刚性多模态3D医学图像配准

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摘要

The non-rigid multi-modal three-dimensional (3D) medical image registration is highly challenging due to the difficulty in the construction of similarity measure and the solution of non-rigid transformation parameters. A novel structural representation based registration method is proposed to address these problems. Firstly, an improved modality independent neighborhood descriptor (MIND) that is based on the foveated nonlocal self-similarity is designed for the effective structural representations of 3D medical images to transform multi-modal image registration into mono-modal one. The sum of absolute differences between structural representations is computed as the similarity measure. Subsequently, the foveated MIND based spatial constraint is introduced into the Markov random field (MRF) optimization to reduce the number of transformation parameters and restrict the calculation of the energy function in the image region involving non-rigid deformation. Finally, the accurate and efficient 3D medical image registration is realized by minimizing the similarity measure based MRF energy function. Extensive experiments on 3D positron emission tomography (PET), computed tomography (CT), T1, T2, and (proton density) PD weighted magnetic resonance (MR) images with synthetic deformation demonstrate that the proposed method has higher computational efficiency and registration accuracy in terms of target registration error (TRE) than the registration methods that are based on the hybrid L-BFGS-B and cat swarm optimization (HLCSO), the sum of squared differences on entropy images, the MIND, and the self-similarity context (SSC) descriptor, except that it provides slightly bigger TRE than the HLCSO for CT-PET image registration. Experiments on real MR and ultrasound images with unknown deformation have also be done to demonstrate the practicality and superiority of the proposed method.
机译：由于相似性度量的构造和非刚性变换参数的求解困难，非刚性多模式三维（3D）医学图像配准非常具有挑战性。提出了一种新颖的基于结构表示的配准方法来解决这些问题。首先，针对3D医学图像的有效结构表示，设计了一种基于偏心非局部自相似性的改进的模态无关邻域描述符（MIND），以将多模式图像配准转换为单模式图像配准。计算结构表示之间的绝对差之和作为相似度。随后，基于中心思想的空间约束被引入到马尔可夫随机场（MRF）优化中，以减少变换参数的数量并限制涉及非刚性变形的图像区域中能量函数的计算。最后，通过最小化基于相似性度量的MRF能量函数，实现了准确，高效的3D医学图像配准。在具有合成变形的3D正电子发射断层扫描（PET），计算机断层扫描（CT），T1，T2和（质子密度）P​​D加权磁共振（MR）图像上的大量实验表明，该方法在以下方面具有更高的计算效率和配准精度：目标配准误差（TRE）项，而不是基于混合L-BFGS-B和猫群优化（HLCSO）的配准方法，熵图像上平方差的总和，MIND和自相似上下文（ SSC）描述符，只是它提供比CTLC图像配准的HLCSO稍大的TRE。还对未知变形的真实MR和超声图像进行了实验，以证明该方法的实用性和优越性。

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