Coefficient random permutation based compressed sensing for medical image compression

Abstract

Compression of medical data remains challenging because of the loss in clarity of compressed images. In medical field, it is necessary to have high image quality in region of interest. This paper presents a Compressed Sensing (CS) method for the compression of medical images. Coefficient random permutation (CRP) based CS is used in this paper for compression of medical images. The different image block has different sparsity. If the nearby pixel values in a block have stronger correlation, then they are strongly sparse, otherwise they are said to be weakly sparse. The main objective of using this method is to provide high quality compressed images thereby maintaining a balanced sparsity of the reconstructed images. As a result performance gain would be high. Experimental results show that CRP based CS helps achieving better PSNR values even with lesser number of measurement values.