The quality evaluation of medical images evaluated by using grey relational coefficients

• Main Authors: You-Cheng Lin, 林宥澄
• Other Authors: Cheng-Hsun Lin
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/qjq27e

碩士 === 中臺科技大學 === 放射科學研究所 === 96 === PURPOSE: The purpose of this study was to develop Grey Relational Coefficient (GRC) system to evaluate compressed medical images. MATERIALS and METHODS: The researcher used Local Grey Relational Coefficients (LGRC) which developed by Hsia, Wen, Wu and Nagai to examine respectively compressed CT, MRI and DR images from compression ratios from 10:1 to 100:1 at 10 different level. The compressed images were also calculated by Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Universal Quality Index (UQI), Mean Structural Similarity (MSSIM), Human Visual System(HVS) and Moran''s Peak Ratio (MPR) algorithms. This was to compare the consequences among GRC and the algorithms. The compression algorithms were JJ2000 and Apollo which are wavelet based algorithm. RESULTS: Four LGRCs were all able to evaluate the compressed image quality changes efficiently. The coefficient of GRC decreased with increasing compression ratios. The Nagai method was superior than others in identification. LGRC had similar tendency comparing with other objective methods, JJ2000 and Apollo had no significant difference at 0.05 between DR and CT images, yet compressed MRI with JJ2000 was superior than that compressed with Apollo (p＜0.05). For the evaluation speed on compressed images, Nagai''s method was faster than UQI and MSSIM. The second for each image was 0.44,1.2 and 1.1 respectively. For the sampling size of 3×3, LGRC was also superior than MSSIM, UQI and MPR with r-values of 0.999,0.994,0.978 and 0.856 respectively. CONCLUSION: LGRC is not affected by choosing sampling size, LGRC is recommended as for evaluating image quality objectively.