The mechanical behavior modeling of human soft biological tissues is a key issue for a large number of medical applications, such as surgery simulation, surgery planning, diagnosis, etc. To develop a biomechanical model of human soft tissues under large deformation for surgery simulation, the adaptive quasi-linear viscoelastic (AQLV) model was proposed and applied in human forearm soft tissues by indentation tests. An incremental ramp-and-hold test was carried out to calibrate the model parameters. To verify the predictive ability of the AQLV model, the incremental ramp-and-hold test, a single large amplitude ramp-and-hold test and a sinusoidal cyclic test at large strain amplitude were adopted in this study. Results showed that the AQLV model could predict the test results under the three kinds of load conditions. It is concluded that the AQLV model is feasible to describe the nonlinear viscoelastic properties of in vivo soft tissues under large deformation. It is promising that this model can be selected as one of the soft tissues models in the software design for surgery simulation or diagnosis.
For tooth segmentation problem on the three-dimensional computed tomography (CT) volume data, this paper proposes a regional adaptive deformable model for tooth structure measurement of CT images. The proposed method combines the automatic thresholding segmentation, CV active contour model, and graph-cut. Firstly, we achieved the segmentation and location of dental crowns by automatic thresholding segmentation. And then by using the above segmentation result as the initial contour, we utilized active contour method to slice gradually the segment of remaining tooth. By incorporating active contour and graph-cut then, we realized the accurate segmentation for tooth root, which is the most difficult to be segmented. The experimental results showed that the proposed tooth structure measurement accurately and automatically segmented dental crowns from CT data, and then rapidly and accurately segmented the tooth neck and tooth root. The structure of tooth could be effectively segmented from CT data by using the proposed method. Experimental results indicated that the proposed method was rather robust and accurate, and could effectively assist the doctor for diagnosis in clinical treatment.
