Multicontrast magnetic resonance imaging (MRI) has shown promise in identifying and characterizing atherosclerotic plaques. One of the limitations of this technique is the lack of a practical automated plaque characterization scheme. In the current study, a prior-information-enhanced clustering (PIEC) technique that utilizes both multicontrast MR images and quantitative T(2) maps is proposed to characterize atherosclerotic plaque components automatically. The PIEC algorithm was assessed on computationally simulated images and multicontrast MRI data of coronary arteries. Multicontrast (T(1)-, T(2)-, partial T(2)-, and proton density-weighted) MR images were acquired from freshly excised human coronary arteries using a 4.7T small-animal scanner. The T(2) distribution for each plaque constituent was measured by exponentially fitting the signal from multiple MR images with different TEs and the same TR. The calculated T(2) distributions were used as the a priori information and combined with the Fuzzy C-Means (FCM)-based clustering algorithm to characterize plaque constituents. The proposed PIEC technique appears to be a promising algorithm for accurate automated plaque characterization.