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Needle Deflection during Insertion into Soft Tissue Based on Virtual Spring Model
Haiyan Du,Yongde Zhang,Jingang Jiang,Yanjiang Zhao 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.1
Needle insertion is the most common procedure of minimally invasive interventions. During insertion into a soft tissue, the needle with bevel tip can deflect due to the asymmetric forces acting on the tip of the needle. In this paper, a mechanics-based model is developed to predict the needle deflection. In the model, the needle is considered as a cantilever beam supported by a series of nonlinear springs each of which has stiffness different from each other. The value of stiffness can be calculated by cutting force acting on the needle tip. Based on the model and the analysis of cutting force and friction force, Rayleigh-Ritz method is used to estimate the amount of needle deflection. Experiment shows that the simulation model can accurately predict the deflection of the bevel-tipped needle.
MRI Segmentation of a Prostate Based on Distance Regularized Level Set Evolution with a Priori Shape
Jingchun Peng,Yongde Zhang,Gang Liu,Jingang Jiang,Yanjiang Zhao 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11
Prostate segmentation from MRI is a necessary first step and plays a key role in different stages of clinical decision making process. In this paper, we propose a MR T1 Image segmentation method of the prostate based on distance regularized level set evolution with a deformable shape prior. To smooth the prostate image to reduce the noise, we introduce a prostate image with Gaussian kernel and get an edge indicator. To avoid the leakage induced on account of prostate boundaries missing, or blending with surrounding tissues, we introduce priori shape information to construct an energy function with a distance regularization term and an external shape energy term containing the edge indicator and minimize it by solving the gradient flow which can be implemented with a finite difference scheme. To verify the MRI segmentation method of a prostate presented in this paper, we utilize the optimal value of parameters λ, μ, α and ε in the distance regularized level set evolution model and the deformable shape prior of prostate to segment a part of images from normal prostate, benign hyperplasia prostate and cancer prostate. The experiment results show that the MRI segmentation method of prostate presented in this paper is effective for different situation of different patients.