Image analysis of placental issues using three-dimensional ultrasound and color power Doppler based on Support Vector Machine
Qi Wang, Diyun Xu, Jianguo Liu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
With the development of medical science, three-dimensional ultrasound and color power Doppler tomography shooting placenta is widely used. To determine whether the fetus's development is abnormal or not is mainly through the analysis of the capillary's distribution of the obtained images which are shot by the Doppler scanner. In this classification process, we will adopt Support Vector Machine classifier. SVM achieves substantial improvements over the statistical learning methods and behaves robustly over a variety of different learning tasks. Furthermore, it is fully automatic, eliminating the need for manual parameter tuning and can solve the small sample problem wonderfully well. So SVM classifier is valid and reliable in the identification of placentas and is more accurate with the lower error rate.