In the interpolation process

biomedical images can be converted to isotropic discretedata. Thus

biomedical images are convenient for manipulation and analysis. Inter-slice interpolation is often required for the 3D reconstruction of medical images. Although many interpolation methods have been proposed in published literature

most of these methods do not consider the gray levels and objective shape variation of images. Moreover

the calculation processes of existing methods are relatively complicated. Therefore

an interpolation algorithm based on the combination of the wavelet and Lagrange polynomials is proposed in the current study. First

images were decomposed by using wavelet analysis to obtainthe positions of wavelet coefficients that belong to the edges. Second

the Lagrange polynomial was applied to interpolate the intensities and positions between the corresponding wavelet coefficients of the cross-sectional images. In this work

the proposed method used three sets of patients' head magnetic resonance images from clinical settings compared with the linear gray-level and cubic interpolation methods.One slice in these data sets is estimated by each interpolationmethod and compared with the original slice by using two measures: number of points of disagreement and mean-squared difference. By using the proposed algorithm

the number of points of disagreement decreased by 10% to 50%

and the mean square error decreased by an average of 3%.The interpolation image smoothed the gray levels and shapes between the original cross-sectional images

thus satisfying the requirements of medical image interpolation. Compared with the linear interpolation and Cubic interpolation methods

the proposed algorithm is able to extract the image shape transform with wavelet transform.Therefore

the proposed method has certain advantages in dealing with the rapid changes of sequential images. In this study

the proposed method can ensure high image quality and reduce calculation errors. The interpolated images can be used to efficiently perform 3D reconstruction for the object tissues of medical images.