A Novel Algorithm by Context Modeling of Medical Image Compression with Discrete Wavelet Transform

Abstract

To overcome the storage, transmission bandwidth, picture archiving and communication constraints and the limitations of the conventional compression methods, the medical imagery needs to be compressed selectively to reduce the transmission time and storage costs while maintaining the high diagnostic image quality. The selective medical image compression provides high spatial resolution and contrast sensitivity requirements for the diagnostic purpose. To fulfill these requirements, a novel approach of context modeling of medical image compression based on discrete wavelet transform has been proposed in this work. In medical images, contextual region is an area which contains the most useful and important information and must be coded carefully without appreciable distortion. The proposed method yields significantly better compression rates with better image quality than the general methods of compression defined in terms of image quality metrics performance. The experimental results have been tested on ultrasound medical images and the results have been compared with the results of standard general Scaling, Maxshift, Implicit, and EBCOT methods of selective image coding where it has been found that the proposed algorithm gives better and improved results based on subjective and objective image quality metrics analysis.