Medical Image Magnification Based on Original and Estimated Pixel Selection Models

The issue of medial image resolution enhancement is one of the most important topics for medical imaging that helps improve the performance of many post-processing aspects like classification and segmentation towards medical diagnosis.

Our aim in this paper is to evaluate different types of pixel selection models in terms of pixel originality in medical image reconstruction problems. A previous investigation showed that selecting far original pixels has highly better performance than using near unoriginal/estimated pixels while magnifying some benchmarks in digital image processing.

In our technical study, we apply two classical interpolators, cubic convolution (CC) and bi-linear (BL), in order to reconstruct medical images in spatial domain. In addition to the interpolators, we use some geometrical image transforms for creating the reconstruction models.

The results clearly demonstrate that despite the absolute preference of the original pixel selection model in the first research, we cannot see this preference in medical dataset in which the results of BL interpolator for both tested models (original and estimated pixel selection models) are approximately the same as each other and for CC interpolator, we only see a relatively better preference for the original pixel selection model.

The current research reveals the fact that selection models are not a general factor in reconstruction problems, and the structure of the basic interpolators is also a main factor which affects the final results. In other words, some interpolators in medical dataset can be affected by the selection models, while, some cannot.