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.