In the process of prenatal ultrasound diagnosis, accurate identification of fetal facial ultrasound standard plane (FFUSP) is essential for accurate facial deformity detection and disease screening, such as cleft lip and palate detection and Down syndrome screening check. However, the traditional method of obtaining standard planes is manual screening by doctors. Due to different levels of doctors, this method often leads to large errors in the results. Therefore, in this study, we propose a texture feature fusion method (LH-SVM) for automatic recognition and classification of FFUSP. First, extract image’s texture features, including Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG), then perform feature fusion, and finally adopt Support Vector Machine (SVM) for predictive classification. In our study, we used fetal facial ultrasound images from 20 to 24 weeks of gestation as experimental data for a total of 943 standard plane images (221 ocular axial planes, 298 median sagittal planes, 424 nasolabial coronal planes, and 350 nonstandard planes, OAP, MSP, NCP, N-SP). Based on this data set, we performed five-fold cross-validation. The final test results show that the accuracy rate of the proposed method for FFUSP classification is 94.67%, the average precision rate is 94.27%, the average recall rate is 93.88%, and the average score is 94.08%. The experimental results indicate that the texture feature fusion method can effectively predict and classify FFUSP, which provides an essential basis for clinical research on the automatic detection method of FFUSP.

中文翻译：

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基于纹理特征融合的胎儿面部超声标准平面识别

在产前超声诊断过程中，准确识别胎儿面部超声标准平面（FFUSP）对于准确的面部畸形检测和疾病筛查至关重要，例如唇腭裂检测和唐氏综合症筛查。然而，获得标准平面的传统方法是由医生手动筛选。由于医生水平不同，这种方法往往会导致结果出现较大误差。因此，在本研究中，我们提出了一种纹理特征融合方法（LH-SVM）用于 FFUSP 的自动识别和分类。首先提取图像的纹理特征，包括局部二值模式（LBP）和定向梯度直方图（HOG），然后进行特征融合，最后采用支持向量机（SVM）进行预测分类。在我们的研究中，我们使用妊娠 20 至 24 周的胎儿面部超声图像作为实验数据，共 943 张标准平面图像（221 个眼轴平面、298 个正中矢状平面、424 个鼻唇冠状平面和 350 个非标准平面、OAP、MSP、NCP , N-SP)。基于这个数据集，我们进行了五重交叉验证。最终测试结果表明，所提方法对 FFUSP 分类的准确率为 94.67%，平均准确率为 94.27%，平均召回率为 93.88%，平均得分为 94.08%。实验结果表明，纹理特征融合方法可以有效地对FFUSP进行预测和分类，为FFUSP自动检测方法的临床研究提供了必要的依据。