the distance between adjacent image elements within a slice is different from the spacing between adjacent image elements in two neighboring slices. Interpolation is the key to convert such anisotropic data into isotropic one. The traditional interpolation methods include grey-level interpolation and shape-based interpolation. But both of them have their own shortcomings. Grey-level interpolation is easy to blur the object's boundary and shape-based interpolation is nearly limited to binary images only. In this paper
in order to solve these questions
we present a new way to interpolate grey-level images
which is based on the shape of these images. First
we use mathematical morphology to acquire the contour of the interpolated image. To each point in this contour
we find the corresponding points in both original images. According to the acquired grey value of the two corresponding points
we use linear interpolation to calculate the grey value of the interpolated point. Once we acquire each point's gray value
we obtain the final interpolated image. The experimental results show that the new method is effective.