Congenital heart disease (CHD) discrimination in fetal echocardiogram based on 3D feature fusion
Liqun Ji, Yun Gu, Kun Sun, Jie Yang, Yu Qiao · 2016
Automatic diagnosis for fetal echocardiography plays an important part in diagnostic aid in the discrimination of congenital heart disease (CHD). Instead of traditional methods analyzing 2D cardiac echo video that need to find the standard view for discrimination, in this paper, we proposed a new system for automatic discrimination of CHD applying 4D original echocardiogram, which avoids the challenging work of searching standard views. We extracted the features of 3D static structure and 3D motion via 3D SIFT and 3D Histogram of Optical Flow (HOF) from the original 4D (3D+T) echocardiogram data, respectively. Bag of Words (BoW) method was employed to construct the quantized feature. Both static and motion features were fused to form the final image representation. One-Class SVM classifier was utilized to discriminate CHD due to the lack of CHD data and the significant difference in all the CHD cases. Experiments on the real data demonstrate the improved discrimination accuracy due to the fused feature.