<br><b>Background</b>: Manual segmentation of brain vessels in a typical MR dataset is both complex and time-consuming; therefore automated approaches are actively sought for, as they also improve the reproducibility of the results.<br><b>Objectives</b>: To present a multiparametric segmentation method (MPS) that,starting from a vessel likeliness function (Vesselness) and R2*map of the brain, applies an Expectation Maximization (EM) algorithm to the bivariate distribution of the data to classify each voxel as belonging or not to veins.<br><b>Methods</b>: <span lang="EN-US" style="line-height: 107%; font-family: Geneva, sans-serif; background-image: initial; background-attachment: initial; background-size: initial; background-origin: initial; background-clip: initial; background-position: initial; background-repeat: initial;">Based on the assumption that a voxel belonging to a vein has high Vesselness</span><span lang="EN-US" style="line-height: 107%; font-family: Geneva, sans-serif; background-image: initial; background-attachment: initial; background-size: initial; background-origin: initial; background-clip: initial; background-position: initial; background-repeat: initial;"> </span><span lang="EN-US" style="line-height: 107%; font-family: Geneva, sans-serif; background-image: initial; background-attachment: initial; background-size: initial; background-origin: initial; background-clip: initial; background-position: initial; background-repeat: initial;">and R2* values, on the log-scale joint histogram of the maps, the voxels whose Vesselness is higher than 0 were assigned to 3 main classes: i) a<br>Gaussian distribution with low R2*and Vesselness (false positives enhanced by Vesselness); ii) a class with R2* value above a given threshold; iii) another Gaussian distribution with medium-high value of R2* and high value of<br>Vesselness,</span><span style="font-family: Geneva, sans-serif; line-height: 107%;">the last two truly corresponding to veins. Through an EM algorithm, the parameters of the 3classes were estimated and the voxels belong-ing to the 2 vessel classes were identified. The performance of the MPS was compared to the Vesselness thresholding (VT) by blindly </span><span style="font-family: Geneva, sans-serif; line-height: 107%;">grading on a 0-5 scale the accuracy of vascular tree depiction.</span><br><b>Results:</b> The semiquantitative analysis clearly showed that MPS achieved greater accuracy in vessel display (scores 4-5 in 88% of the test-sample) than VT (scores 2-3 in 74% of the test-sample). In particular, false positives were the main pitfall of VT compared to MPS.<br><b>Conclusions:</b> Combining the information obtained from Vesselness and R2* maps, the MPS substantially increased sensitivity and specificity of the monoparametric tresholding based on vesselness images only.<br>