With the advancement of structured-light cameras, surgical robots equipped with such cameras have been utilized for lesion localization during surgeries. Achieving precise registration between CT images and point cloud data remains a challenge. This study proposes a registration method for CT images and point cloud data. Firstly, the CT images are converted into a point cloud representation, and Feature Histograms (FPFH) are computed based on the point cloud's normal vectors. Subsequently, the Fast Global Registration (FGR) algorithm is employed to perform coarse registration of the point cloud. Finally, the Iterative Closest Point (ICP) algorithm is utilized for fine registration of the point cloud data. Experimental evaluation is conducted using CT images of a human brain model and point cloud data obtained from a structured-light camera. The results demonstrate a favorable registration performance. The coarse registration facilitated by the FGR algorithm serves as an effective initialization for the ICP algorithm, thereby enhancing the convergence speed and accuracy of the fine registration process.