This study was to explore the denoising and segmentation effect of dual-domain image denoising (DDID) algorithm, and the Galois field (GF) and nonlocal means (NLM) algorithms were introduced for comparative analysis. 40 primiparas in the hospital from January 2018 to January 2020 were divided into an experimental group (caesarean section (CS), group E) and a control group (vaginal delivery (VD), group C). The peak signal-to-noise ratio (PSNR) and segmentation parameters of DDID algorithm were compared with GF algorithm and NLM algorithm. It was found that the DDID showed higher overall accuracy (OA) and lower false positive rate (FPR) and false negative rate (FNR). The PSNR of DDID was higher than the other two algorithms. GF algorithm showed the highest edge retention index (ERI). The incidence of pelvic organ prolapse (POP) in group E and group C was 9/20 (45%) and 5/20 (25%), respectively, with extreme difference (). Evaluation of the effects of delivery on the pelvis of primiparas with MRI three-dimensional (3D) reconstructed images based on the optimized DDID showed a superior and stable denoising effect and good segmentation, so it was worthy of clinical promotion and application.

中文翻译：

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MRI三维重建图像的优化双区域滤波算法，用于评估分娩对初产妇骨盆的影响

本研究旨在探讨双域图像去噪（DDID）算法的去噪和分割效果，并介绍了伽罗瓦域（GF）算法和非局部均值（NLM）算法进行比较分析。从2018年1月至2020年1月，将40例初产妇分为实验组（剖腹产（CS），E组）和对照组（阴道分娩（VD），C组）。将DDID算法的峰值信噪比（PSNR）和分割参数与GF算法和NLM算法进行了比较。发现DDID显示出更高的总体准确度（OA）和更低的误报率（FPR）和误报率（FNR）。DDID的PSNR高于其他两种算法。GF算法显示出最高的边缘保留指数（ERI）。骨盆器官脱垂（POP）的发生率E和C组分别为9/20（45％）和5/20（25％），两者之间存在极高的差异（）。基于优化的DDID的MRI三维（3D）重建图像评估分娩对初产妇骨盆的影响，显示出优异而稳定的去噪效果和良好的分割效果，因此值得临床推广和应用。