Enhancing Quality of MR Image Based on Wavelet Algorithm
Sheikh Md. Rabiul Islam, Xu Huang, Kim Le, Mingyu Liao · International Journal of Computational Intelligence Research · 2013
In the field of medical diagnostic, magnetic resonance (MR) image is an important and popular medical image, which technically required enormous data to be stored and transmitted. In order to make accurate diagnostic for the patients those data from the MR images need to be high accuracy and completeness. Various algorithms have been proposed to improve the performance of the compression scheme. One of our contributions in this paper is to demonstrate the choice of decomposition level is playing a very important role in achieving superior wavelet compression performances. We extended the commonly used algorithms to image compression and compare its performance. For the best image compression performance, lifting based Cohen-Daubechies-Feauveau wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform is used, which coupled with Set Partition in Hierarchical Trees (SPIHT) coding algorithm and entropy coding techniques. The final simulations showed that all those used technologies have made a large reduction of image size occur. It needs to be highlighted that the compression ratio has been significantly improved by 99%, together with highest PSNR values and MSSIM by overall recognition rate 96.17%.