It's a great challenge to analyze infant brain MR images due to the small brain size and low contrast of the developing brain tissues. We have developed an Infant Brain Extraction and Analysis Toolbox (iBEAT) for various processing of magnetic resonance (MR) images of infant brains. Several major functions generally used in infant brain analysis are integrated in iBEAT, including image preprocessing, brain extraction, tissue segmentation, and brain labeling. The functions of brain extraction, tissue segmentation, and brain labeling are provided respectively by three state-of-the-art algorithms. First, a learning-based meta-algorithm which integrates a group of brain extraction results generated by the two existing brain extraction algorithms (BET and BSE) was implemented in iBEAT for extraction of infant brains from MR images. Second, a level-sets-based tissue segmentation algorithm that utilizes multimodality information, cortical thickness constraint, and longitudinal consistency constraint was also included in iBEAT for segmentation of infant brain tissues. Third, HAMMER (standing for Hierarchical Attribute Matching Mechanism for Elastic Registration) registration algorithm was further included in iBEAT to label regions of interest (ROIs) of infant brain images by warping the pre-labeled ROIs of a template to the infant brain image space. By integration of these state-of-the-art methods, iBEAT is able to segment and label infant brain MR images accurately. Moreover, it can process not only single-time-point images for cross-sectional studies, but also multiple-time-point images of the same infant for longitudinal studies. The performance of iBEAT has been comprehensively evaluated with hundreds of infant brain images. A Linux-based standalone package of iBEAT is freely available at http://www.nitrc.org/projects/ibeat .