Robust Classification of MR Brain Images Based on Multiscale Geometric Analysis

The widely used feature representation scheme for magnetic resonance (MR) image classification based on low-frequency subband (LFS) coefficients of wavelet transform (WT) is ineffective in presence of common MR imaging (MRI) artifacts (small rotation, low dynamic range etc.). The directional information present in the high-frequency subbands (HFSs) can be used to improve the performance. Moreover, little attention has been paid to the newly developed multiscale geometric analysis (MGA) tools (curvelet, contourlet, and ripplet etc.) in classifying brain MR images. In this paper, we compare various multiresolution analysis (MRA)/MGA transforms, such as traditional WT, curvelet, contourlet and ripplet, for brain MR image classification. Both the LFS and the high-frequency subbands (HFSs) are used to construct image representative feature vector invariant to common MRI artifacts. The investigations include the effect of different decomposition levels and filters on classification performance. By comparing results, we give the best candidate for classifying brain MR images in presence of common artifacts.