A fast level set method for propagating interfaces
Journal of Computational Physics
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Fast and accurate edge-based segmentation with no contour smoothing in 2-D real images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Segmentation of 3D brain structures using the Bayesian generalized fast marching method
BI'10 Proceedings of the 2010 international conference on Brain informatics
3D vector flow guided segmentation of airway wall in MSCT
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Estimation of 2D jump location curve and 3D jump location surface in nonparametric regression
Statistics and Computing
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3D object segmentation is important in computer vision such as target detection in biomedical image analysis. A new method, called B-Surface algorithm, is generated for 3D object segmentation. An improved 3D external force field combined with the normalized GVF is utilized. After the initialization of a surface model near the target, B-Surface starts to deform to locate the boundary of the object. First, it overcomes the difficulty that comes from analyzing 3D volume image slice by slice. And the speed of B-Surface deformation is enhanced since the internal forces are not needed to compute in every iteration deformation step. Next, the normal at every surface point can be calculated easily since B-Surface is a continuous deformable model. And it has the ability to achieve high compression ratio (ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Experimental results and analysis are presented in this paper. We can see that the B-Surface algorithm can find the surface of the target efficiently.