Abstract
This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r > 0.98, p < 0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.
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References
Huang HK: PACS—Picture Archiving and Communication Systems in Biomedical Imaging. VCH Publishers, New York, USA, 1996
Zhang Y, Pham BT, Eckstein MP: Automated Optimization of JPEG 2000 Encoder Options Based on Model Observer Performance for Detecting Variable Signals in X-Ray Coronary Angiograms. IEEE Trans Med Imaging 23:459–474, 2004
Erickson BJ: Irreversible compression of medical images. J Digit Imaging 15:5–14, 2002
Kim KJ, Kim B, Mantiuk R, Richter T, Lee H, Kang HS, Seo J, Lee KH: A comparison of three image fidelity metrics of different computational principles for JPEG2000 compressed abdomen CT images. IEEE Trans Med Imaging 8:1496–1503, 2010
MacMahon H, et al: Data Compression: Effect on diagnostic accuracy in digital chest radiography. Radiology 178:175–179, 1991
Shiao YH, Chen TJ, Chuang K, Lin CH, Chuang CC: Quality of Compressed Medical Images. J Digit Imaging 20:149–159, 2007
Kim KJ, Kim B, Choi SW, Kim YH, Hahn S, Kim TJ, Cha SJ, Bajpai V, Lee KH: Definition of Compression Ratio: Difference Between Two Commercial JPEG2000 Program Libraries. Telemed e-Health 14:350–354, 2008
Rabbani M, Joshi R: Signal Processing: Image Commun 17:3–48, 2002
Brennecke R, Burgel U, Rippin G, Post F, Rupprecht HJ, Meyer J: Comparison of image compression viability for lossy and lossless JPEG and Wavelet data reduction in coronary angiography. Int J Cardiovasc Imaging 17:1–12, 2001
Ebrahimi F, Chamik M, Winkler S: JPEG vs. JPEG2000: an objective comparison of image encoding quality. Applications of Digital Image Processing XXVII. Proc SPIE 5558:300–308, 2004
Eikelboom RH, Yogesan K, Barry CJ, Constable IJ, Tay-Kearney ML, Jitskaia L, House PH: Methods and limits of digital image compression of retinal images for telemedicine. Investig Ophthalmol Vis Sci 41:1916–1924, 2000
Hui OT, Besar R: Medical image compression using JPEG2000 and JPEG: a comparison study. J Mech Med Biol 2:313–328, 2002
I'smail A, Bulent S, Khalid S: Statistical evaluation of image quality measures. J Electron Imaging 11:206–223, 2002
Iyriboz TA, Zukoski MJ, Hopper KD, Stagg PL: A comparison of wavelet and Joint Photographic Experts Group lossy compression methods applied to medical images. J Digit Imaging 12(2 Suppl 1):14–17, 1999
Janhom A, Stelt PF, Sanderink GCH: A comparison of two compression algorithms and the detection of caries. Dentomaxillofac Radiol 31:257–263, 2000
Kalyanpur A, Neklesa VP, Taylor CR, Daftary AR, Brink AR: Evaluation of JPEG and wavelet compression of body CT images for direct digital teleradiologic transmission. Radiology 217:772–779, 2000
Li F, Sone S, Takashima S, Kiyono K, Yang ZG, Hasegawa M, Kawakami S, Saito A, Hanamura K, Asakura K: Effects of JPEG and wavelet compression of spiral low-dose CT images on detection of small lung cancers. Acta Radiol 42:156–160, 2001
18.Przelaskowski A. Hybrid Vector Measures of Compressed Medical Images. SPIE Symposium Medical Imaging: Image Perception and Performance.2001. http://www.ire.pw.edu.pl/~arturp/Publikacje/ap_HVM.pdf.
Ricke J, Maass P, Lopez HE, Liebig T, Amthauer H, Stroszczynski C, Schauer W, Boskamp T, Wolf M: Wavelet versus JPEG (Joint Photographic Expert Group) and fractal compression. Impact on the detection of low-contrast details in computed radiographs. Investig Radiol 33:56–463, 1998
Slone RM, Foos DH, Whiting BR, Muka E, Rubin DA, Pilgram TK, Kohm KS, Young SS, Ho P, Hendrickson DD: Assessment of visually lossless irreversible image compression: comparison of three methods by using an image-comparison workstation. Radiology 215:543–553, 2000
Chen TJ, Chuang KS, Chang JH, Shiao YH, Chuang CC: A blurring index for medical images. J Digit Imaging 19:118–125, 2006
Chen TJ, Chuang KS, Jay W, Chen SC, Hwang IM, Jan ML: A novel image quality index using Moran I statistics. Phys Med Biol 48:131–137, 2003
Wang Z, Bovik AC: A universal image quality index. IEEE Signal Process Lett 9:81–84, 2002
Cliff AD, Ord JK: Spatial process: models and applications. Pion, London, UK, 1981
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The authors would like to thank Prof. KS Chuang of the National Tsing-Hua University for his help in editing this paper.
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Chen, TJ., Lin, SC., Lin, YC. et al. JPEG2000 Still Image Coding Quality. J Digit Imaging 26, 866–874 (2013). https://doi.org/10.1007/s10278-013-9603-x
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DOI: https://doi.org/10.1007/s10278-013-9603-x