Fully Automatic Segmentation of White Matter Lesions from Multispectral Magnetic Resonance Imaging Data

Abstract: A fully automatic white matter lesion segmentation method has been developed and evaluated. The method uses multispectral magnetic resonance imaging (MRI) data (T1,T2 and Proton Density). First fuzzy c means (FCM) was used to segment normal brain tissues (white matter,grey matter, and cerebrospinal fluid). The holes in normal white matter were used to sample the WML intensities in the different images. The segmentation of WML was optimized by a graph cut approach. The method was trained by using 9 manually segmented datasets and evaluated by comparison to 11 other manually segmented, and visually evaluated, datasets. The graph cut part of the automatic segmentation requires, on average, 30 seconds per dataset. The results correlated well (r=0.954) to a manually created reference that was supervised by two neuro radiologists. Key Words: White matter lesion, automatic segmentation, graph cuts, MRI, PIVUS