Ultrasonic image classification based on ICA&SVM
Weishi Chen, Tiejun Liu, Wang Baofa · 2010 Sixth International Conference on Natural Computation · 2010
Unbalance of gender ratio at birth has been a serious phenomenon in China. To solve this problem, a scheme for ultrasonic image classification is proposed for preventing fetus gender examination with non-medical purposes. Tens of thousands of ultrasonic images with and without sexual organs are collected to establish a professional database. These images are preprocessed firstly by cropping, de-noising and compression. And then, independent component analysis (ICA) is applied for feature extraction under two architectures, which give local and global information respectively. After training of selected samples, a support vector machine (SVM) classifier which combined the two ICA representations is established for recognition, and a good performance is given for testing data. Finally, some new technique is suggested for algorithm improvement in the future.