الفهرس 
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Abstract
Medical image acquisition and storage is a very vital issue in the medical field. The major objective is to store images without losing their discriminative features. This thesis presents an efficient scenario for medical image acquisition and storage based on a decimation interpolation strategy. The basic idea is to perform decimation to save the storage capacity, and then interpolation to restore image. Different interpolation schemes are studied and compared for this purpose, especially, polynomial-based and inverse techniques. In addition, a sophisticated method for quality evaluation of the obtained results based on Scale-Invariant Feature Transform (SIFT) is presented. Finally, the ability to recognize certain patterns subject to the proposed strategy is investigated through deep learning.