Traumatic brain injury (TBI) is a major cause of disability and death. Speed and accuracy are vital in diagnosing TBI for which computer-aided imaging analysis may speedup and improve the efficiency of diagnosis and help reduce mortality, long-term complications, and the associated costs. However, developing such a system is challenging due to some factors such as the inherent noise associated with obtaining the images, artifacts and quality of the images. An automated system that can preliminary identify, localize and quantify the imaging features of TBI would be beneficial in guiding real-time clinical diagnosis as well as for quality assurance. In this paper we propose an automated system to segment the hematoma region from CT images. The proposed method first performs denoising and image enhancement and then by developing a Gaussian mixture model, segmentation is carried out. We show the performance of the system by comparing the results with ground truth generated by specialists.