Abstract
To minimize patients' radiation exposure while taking CT images, one normally limits the distance of two consecutive images between 4mm~8mm or even larger. Using image interpolation techniques to reduce the gap between image slices can enhance the quality of the reconstructed 3D models. Linear interpolation and shape-based interpola-tion are the two most popular approaches. While linear interpolation is easy to implement with a short processing time, the quality of the generated image is relatively poor. On the other hand, shape-based interpolation can produce better quality images with much more time. This paper presents three hybrid interpolation methods that take the advantages of the above two approaches. Five main processes are applied to grey-level medical images in our experiments: threshold-ing, distance transform, linear interpolation, binary conversion, and enlarge increment. The results show significant improvements on 3D image quality and processing speed that can help doctors make their diagnoses easier, faster, and more precise.