Typically

the image data we get are anisotropic

that is

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.