Utilization of decimation interpolation strategy for medical image communication and storage

Abstract

In storing large databases of images such as medical databases, the required memory size becomes a great challenge. This paper presents a framework for reducing the size of large stored medical images. Among available methods, the Joint Photographic Experts Group (JPEG) has moderate performance as it is a lossy standard. It only compresses a single picture with intra-coding, and it does not utilize the inter-frame correlation among pictures. The Set Partitioning in Hierarchical Trees (SPIRT) algorithm is a refined version of the Embedded Zero Wavelet (EZW) algorithm. It can perform better with higher compression ratios for a wide variety of images than those of the EZW. In this paper, the decimation is adopted as a compression process to minimize the size of stored images. Interpolation is used to recover images for further processing. Different interpolation schemes can be used for this purpose. A comparative study between different polynomial interpolation methods is presented for the objective of reconstructing the images. The experimental results show that the proposed decimation interpolation strategy yields good reconstruction performance, presents good compression ratios in the decimation process, and introduces acceptable PSNR values in the reconstruction process.