The presence of cavities in the upper lung zones is an important indicator of highly infectious Tuberculosis (TB). Diagnoses performed by the radiologists are labor intensive and of high inter-reader variation. After analyzing the existing computer-aided detection techniques, we propose an fully automated TB cavity detection system which combines a 2D Gaussian-model-based template matching (GTM) for candidates detection with Hessian-matrix-based image enhancement (HIE) for the following cavity segmentation and feature extraction. Experimental results demonstrate that our approach outperforms the existing TB cavity detection technique with higher accuracy and lower false positive rate.