School of Computer and Information Science, Southwest University, Chongqing, China.
Front Public Health. 2022 Jun 1;10:863307. doi: 10.3389/fpubh.2022.863307. eCollection 2022.
With the continuous improvement of medical imaging equipment, CT, MRI and PET images can obtain accurate anatomical information of the same patient site. However, due to the fuzziness of medical image physiological evaluation and the unhealthy understanding of objects, the registration effect of many methods is not ideal. Therefore, based on the medical image registration model of Partial Volume (PV) image interpolation method and rigid medical image registration method, this paper established the non-rigid registration model of maximum mutual information Novel Partial Volume (NPV) image interpolation method. The proposed NPV interpolation method uses the Davidon-Fletcher-Powell algorithm (DFP) algorithm optimization method to solve the transformation parameter matrix and realize the accurate transformation of the floating image. In addition, the cubic B-spline is used as the kernel function to improve the image interpolation, which effectively improves the accuracy of the registration image. Finally, the proposed NPV method is compared with the PV interpolation method through the human brain CT-MRI-PET image to obtain a clear CT-MRI-PET image. The results show that the proposed NPV method has higher accuracy, better robustness, and easier realization. The model should also have guiding significance in face recognition and fingerprint recognition.
随着医学影像设备的不断进步，CT、MRI 和 PET 图像可以获取同一患者部位的精确解剖信息。然而，由于医学图像生理评估的模糊性和对物体的不健康理解，许多方法的配准效果并不理想。因此，本文基于部分容积（PV）图像插值方法和刚性医学图像配准方法的医学图像配准模型，建立了基于最大互信息的非刚性配准模型——新型部分容积（NPV）图像插值方法。所提出的 NPV 插值方法使用 Davidon-Fletcher-Powell 算法（DFP）算法优化方法来求解变换参数矩阵，并实现浮动图像的精确变换。此外，使用三次 B 样条作为核函数来改进图像插值，这有效地提高了配准图像的准确性。最后，通过人脑 CT-MRI-PET 图像将所提出的 NPV 方法与 PV 插值方法进行比较，得到了清晰的 CT-MRI-PET 图像。结果表明，所提出的 NPV 方法具有更高的准确性、更好的鲁棒性和更简单的实现。该模型在人脸识别和指纹识别方面也应该具有指导意义。
