Tissue Detection in MR Images Based on an Improved SVM

Abstract

In the brain Magnetic Resonance (MR) images, the boundary of each encephalic tissue is highly irregular. It is difficult to accurately detect the encephalic tissues. Owing to its powerful capacity in solving non-linearity problems, Support Vector Machine (SVM) has been widely used in object detection. The conventional SVMs, however, assume that each feature of a sample has the same importance degree for the detection result, which is not a true representation of real applications. In addition, the parameters of the SVM and its kernel function also affect detection result. In this study, Immune Algorithm (IA) was introduced in searching for the optimal feature weights and the parameters simultaneously. An Immune Feature Weighted SVM (IFWSVM) method was used to detect encephalic tissues in MR images. Theoretical analysis and experimental results showed that the IFWSVM has better performance than the conventional methods.