Automatic Segmentation of White Matter Lesions Using SVM and RSF Model in Multi-channel MRI

Abstract

Brain lesions, especially White Matter Lesions, are not only associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WMLs in large clinical trials is becoming more and more important. Based on intensity features and tissues' prior, we develop a computer-assisted WMLs segmentation method, with the expectation that our approach would segment WMLs in Magnetic Resonance Imaging (MRI) sequences without user intervention. We first train a SVM nonlinear classifier to classify the MRI data voxel-byvoxel. In detail, the attribute vector is constructed by the intensity features extracted from Multi-channel MRI sequences, i.e. fluid attenuation inversion recovery (FLAIR), T1-weighted, T2-weighted, proton density-weighted (PD), and the tissues' prior provided by partial volume estimate (PVE) images in native space. Based on the prior that the lesions almost exist in white matter, we then present an algorithm to eliminate the false-positive labels. Subsequent further segmentation through Region-Scalable Fitting (RSF) evolution on FLAIR sequence is employed to effectively segment precise lesions boundary and detect missing lesions. Compared with the manual segmentation results from an experienced neuroradiologist, experimental results for real images show desirable performances and high accuracy of the proposed method.