Abstract
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 F1 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.
Highlights
Ultrasound has been used for prenatal observation, measurement, and diagnosis of fetal diseases for nearly 30 years due to its advantages of low cost, portability, no radiation, and real-time imaging capabilities
It could be clearly seen that each group performed well in all evaluation indexes, and all indexes were above 91.00%
To solve the problem that the traditional method of obtaining fetal facial ultrasound standard plane (FFUSP) is highly dependent on the doctor’s seniority, energy, and other aspects, and save time and human resources; in this study, we used the fusion of Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) to extract the texture features of the image and Support Vector Machine (SVM) classifier for recognition and classification to achieve the rapid classification of FFUSP
Summary
Ultrasound has been used for prenatal observation, measurement, and diagnosis of fetal diseases for nearly 30 years due to its advantages of low cost, portability, no radiation, and real-time imaging capabilities. Due to the large population base in our country, there are many abnormal births every year, causing numerous medical disputes and a heavy burden on the family and society, affecting the quality of the national population [4]. Prenatal diagnosis is the key to screening for fetal abnormalities. Parents-to-be can make reproductive decisions for their unborn children on a legal basis based on the screening results [5]. Taking effective measures to improve prenatal ultrasound diagnosis and reduce the missed diagnosis rate of fetal malformations is of great value in reducing newborn congenital disabilities
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