Shape-based grey-level image interpolation

The three-dimensional (3D) object data obtained from a CT scanner usually h ave unequal sampling frequencies in the x-, y- and z-directions. Generally, the 3D data are first interpolated between slices to obtain isotropic reso lution, reconstructed, then operated on using object extraction and display algorithms. The traditional grey-level interpolation introduces a layer of intermediate substance and is not suitable for objects that are very diffe rent from the opposite background. The shape-based interpolation method tra nsfers a pixel location to a parameter related to the object shape and the interpolation is performed on that parameter. This process is able to achie ve a better interpolation but its application is limited to binary images o nly, in this paper, we present an improved shape-based interpolation method for grey-level images. The new method uses a polygon to approximate the ob ject shape and performs the interpolation using polygon vertices as referen ces. The binary images representing the shape of the object were first gene rated via image segmentation on the source images. The target object binary image was then created using regular shape-based interpolation. The polygo n enclosing the object for each slice can be generated from the shape of th at slice. We determined the relative location in the source slices of each pixel inside the target polygon using the vertices of a polygon as the refe rence. The target slice grey-level was interpolated from the corresponding source image pixels. The image quality of this interpolation method is bett er and the mean squared difference is smaller than with traditional grey-le vel interpolation.