Wavelet Based Features for Ultrasound Placenta Images Classification

Medical Diagnosis is the utmost need of an hour. Gestational Diabetics in women represents the second leading cause of yielding children born with birth defects. The ultrasound images are usually low in resolution making diagnosis difficult. Specialized tools are required to assist the medical experts to categorize and diagnose diseases to accuracy. If the anomalies in the ultrasound images are detected in the preliminary screening of placenta, fetal loss could be minimized. This pilot study was carried out to find the feasibility for detecting anomalies in placental growth due to the implications of gestational diabetics by considering the stereo image mapping based on wavelet analysis for 2D reconstruction. The research uses wavelet based methods to extract features from the ultrasonic images of placenta. The shape of the placenta is generated using the back propagation network. Euclidean distance classifier is used for classifying the ultrasonic images of placenta.