We present a new method for automated characterization of atherosclerotic plaque composition in ex vivo MRI. It uses MRI intensities as well as four other types of features: smoothed, gradient magnitude and Laplacian images at several scales, and the distances to the lumen and outer vessel wall. The ground truth for ¿brous, necrotic and calci¿ed tissue was provided by histology and µCT in 12 carotid plaque specimens. Semi-automatic registration of a 3D stack of histological slices and µCT images to MRI allowed for 3D rotations and inplane deformations of histology. By basing voxelwise classi¿cation on different
combinations of features, we evaluated their relative importance. To establish whether training by 3D registration yields different results than training by 2D registration, we determined plaque composition using (1) a 2D slice-based
registration approach for three manually selected MRI and histology slices per specimen, and (2) an approach that uses only the three corresponding MRI slices from the 3D-registered volumes. Voxelwise classi¿cation accuracy was best when all features were used (73.3 ± 6.3%) and was signi¿cantly better than when only original intensities and distance features were used (Friedman,
p < 0.05). Although 2D registration or selection of three slices from the 3D set slightly decreased accuracy, these differences were non-signi¿cant.