Automatic Segmentation of White Matter Lesion from Multi-channel MRI Data Based on Energy Minimization

Abstract

The detection of multiple sclerosis lesion is important for many neuroimaging studies. In this paper, a new automatic algorithm for lesion segmentation based on the multi-channel MR images (T1w, T2w and FLAIR image) is proposed, which utilizes the unique and complementary intensity information of multi-channel MR images. In this method, the observed multi-channel MR images are modeled as a vector valued image. The image in each channel consists of two multiplicative components: a smooth varying bias filed vector and a piecewise approximately constant true image vector. An energy function of this vector valued image is defined in term of the property of true image and bias field. The energy minimization is proposed for seeking the optimal segmentation result of lesions. Our method is applied to the real multi-channel MR images and compared with two sets of manual segmentation followed by the quantitative evaluation. The experimental results show that our approach is effective and robust for the lesion segmentation.