Contours modeling of fascicular groups from micro-computed tomography images of peripheral nerves

Abstract The objective is to explore the appropriate method to establish the mathematical model of fascicular groups' contours from micro-CT images of peripheral nerves during the nonsplitting/merging phase. The original contours of fascicular groups from the micro-CT image were described as the discrete pixel points. All discrete pixel points of shapes were extracted into a data set through image processing. The data set was modeled by Bezier, B-spline method, respectively, in which each discrete point was used as a control point for modeling. In the Bezier method, the contour of a nerve bundle needs more than two different Bezier curves to model, making the junction points between two models discontinuous. The contour model described by B-spline is very close to the original contour of nerve bundles when all discrete points are used as the control points. The models described by B-spline have different terms and parameters, making it difficult to calculate in the following research. When the third-order quasi-uniform B-spline method is employed, all nerve bundles models have the same number of terms. The modeling error of third-order quasi-uniform B-spline is less than 3% when the Dice coefficient is more than 95%, and the appropriate number of sampling times is 21. The modeling accuracy is improved with increased sampling times when it is less than 21. However, the modeling accuracy remains stable while the number of sampling times is more than 21. The third-order quasi-uniform B-spline is more efficient in modeling nerve bundles' contour, which is more accurate and straightforward.