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.

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971T (30 October 2009);

Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)