Analysis of Image Compression by Minimum Relative Entropy (MRE) and Restoration through Weighted Region Growing Techniques for Medical Images

— In recent years, there is a spurt of publications on Information Theory based Image Compression Techniques. Specific regional characteristics of the images are associated with Information clusters. In this paper a novel approach of information theory based Minimum Relative Entropy (MRE) and Entropy methods for image compression are discussed. A two stage compression process is performed through homogenous MRE method, and heterogeneous MRE. The compressed images are reconstructed through Region growing techniques. The performance of image compression and restoration is analyzed by the estimation of parametric values such as Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Higher the PSNR better the reconstruction process. Six radiographic medical images of various sizes are analyzed and Maximum PSNR of 33 is achieved.