Application of Independent Component Analysis in MR Image Segmentation
The performance of supervised learning classifier could be greatly increased by Compressing redundant image features information. This paper proposed a new feature extraction algorithm using independent component analysis (ICA) for classification problems. Firstly extract original gray and texture image features (original features), then use ICA for obtaining independent components of the original features to compress redundant information, the new features were classified with Support Vector Machines (SVM). The experiment results shows that the use of new features based on ICA greatly reduce the dimension of feature space and upgrade the performance of classifying systems. With the proposed ICA method, 2.17％ higher accuracy was obtained than that of the original image features.
Independent Component Analysis Support Vector Machines Feature Eztraction Image Segmentation
Zehui Li Shengdong Nie Zhaoxue Chen
Optical & Electronic Information Engineering College College of Medical Instrumentation and Food Stu College of Medical Instrumentation and Food Stuff University of Shanghai for science and Technology
国际会议
上海
英文
2708-2711
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）