Ripplet Transform Type II based Efficient Medical Image Compression

Abstract

Hospitals produce a number of images per diagnosis and this can lead to produce the 5GB to 15 GB data. It increases the difficulties for a hospital storage system to store, manage and to transmit these images. Among the proposed compression methods, much interest has been focused on achieving good compression ratios and high Peak Signal to Noise Ratio (PSNR), and little work has been done on resolving 2D singularities along image edges with efficient representation of images at different scales and different directions. Grounded on this fact, this paper proposes a compression method for medical images by representing singularities along arbitrarily shaped curves without sacrificing the amount of compression. This method uses a recently introduced family of directional transforms called Ripplet transform. Usually the coarser version of an input image is represented using base, but discontinuities across a simple curve affect the high frequency components and affect all the transform coefficients on the curve. Hence these transforms do not handle curve discontinuities well. By defining the scaling law in a more broader scope and more flexible way, Ripplet Transform is formed as a generalization of Curvelet transform, by adding two tunable parameters i.e support of Ripplets and degree of Ripplets .The inherent properties of Ripplet transform in conjunction with the coding of coefficients using Huffman Encoder provide efficient representation of edges in images and thereby achieving a high quality compressed image.