In oral surgery, segmentation of teeth has important application value. However, due to the ambiguity of tooth boundary, the adhesion of adjacent teeth, and the flexible change of topological structure in dental Computer Tomography (CT) images, it is very difficult to achieve the accurate segmentation. To provide a useful reference for researches, this paper explored the search progress of dental CT image segmentation base on level set methods, summarized the traditional methods of dental CT images segmentation, introduced the level set theory briefly, introduced the details of level set methods for teeth segmentation in recent years, studied the energy terms in level set function, and implemented some contrast experiments. In the dental CT images segmentation based on level set method, the energy terms mainly included competitive energy, edge energy, shape prior energy, global intensity prior energy and local intensity energy. The experimental results show that the performance of hybrid model of the level set method is the best. The segmentation accuracies of incisor and molar teeth were 88.92% and 92.34% respectively. Compared to the method of adaptive threshold and level set without re-initialization, the accuracy of hybrid model improved more than 10% overall. With the utilization of image information and prior knowledge, it is expected to improve the accuracy of segmentation by optimizing and innovating the energy term in the level set function.

The current medical image registration technology could not meet the real-time requirements for clinical diagnosis and treatment. Graphic Processing Unit (GPU) accelerated medical image registration technology was reviewed and discussed for this problem in this paper. The paper summarized GPU general purpose computation, studied current technology of medical image registration which based on GPU acceleration with the essential framework of medical image registration as main line, and implemented Positron Emission computed Tomography (PET) and Computed Tomography (CT) image registration experiments respectively on Central Processing Unit (CPU) and GPU computing platforms. The Normalized Mutual Information (NMI) value of GPU accelerated medical image registration based on Free Form Deformation ( FFD) and NMI was slightly smaller than that of CPU method, but the registration efficiency is 12 times than CPU method. Except keeping high registration accuracy, GPU accelerated medical image registration algorithms also get a lot of ascension in terms of registration speed.