Abstract
The authors’ goal was to explore the impact of image compression algorithm and ratio, image luminance, and viewing distance on radiologists’ perception of reconstructed image fidelity. Five radiologists viewed 16 sets of four hard-copy chest radiographs prepared for secondary interpretation. Each set included one uncompressed, and three compressed and reconstruted images prepared using three different algorithms but the same compression ratio. The sets were prepared using two subjects, four compression ratios (10∶1, 20∶1, 30∶1, 40∶1), and two luminance levels (2,400 cd/m2, standard lightbox illumination, and 200 cd/m2, simulating a typical CRT display). Readers ranked image quality and evaluated obviousness and clinical importance of differences. Viewing distances for image screening, inspection, and comparison were recorded. At 10∶1 compression, the compressed and uncompressed images were nearly indistinguishable; the three algorithms were very similar, and differences were rated “not obvious” and “not important.” At higher compression, readers consistently preferred uncompressed images, with notable differences between algorithms. The obviousness and clinical importance of differences were rated higher at lightbox luminance. Viewing distances appeared to be idiosyncratic
Key Words: thorax, radiography, images, compression, fidelity
Full Text
The Full Text of this article is available as a PDF (722.3 KB).
References
- 1.Bramble JM, Huang HK, Murphey MD. Image data compression. Invest Radiol. 1988;23:707–712. doi: 10.1097/00004424-198810000-00001. [DOI] [PubMed] [Google Scholar]
- 2.Ishigaki T, Sakuma S, Ikeda M, et al. Clinical evaluation of irreversible image compression: Analysis of chest imaging with computed radiography. Radiology. 1990;175:739–743. doi: 10.1148/radiology.175.3.2343125. [DOI] [PubMed] [Google Scholar]
- 3.MacMahon H, Doi K, Sanada S, et al. Data compression: Effect on diagnostic accuracy in digital chest radiography. Radiology. 1991;178:175–179. doi: 10.1148/radiology.178.1.1984299. [DOI] [PubMed] [Google Scholar]
- 4.Aberle DR, Gleeson F, Sayre JW, et al. The effect of irreversible image compression on diagnostic accuracy in thoracic imaging. Invest Radiol. 1993;28:398–403. doi: 10.1097/00004424-199305000-00002. [DOI] [PubMed] [Google Scholar]
- 5.Goldberg MA, Pivovarov M, Mayo-Smith WW, et al. Application of wavelet compression to digitized radiographs. AJR. 1994;163:463–468. doi: 10.2214/ajr.163.2.8037051. [DOI] [PubMed] [Google Scholar]
- 6.Bunge RE, Ducca AT, Properzio WS: Exposure in chest radiology—NEXT results in optimization of chest radiography, US Dept. HHS Publication (FDA) 80–8124, 172–181, 1980
- 7.Jones PW, Daly S, Gaborski RS, et al. Comparative study of wavelet and DCT decompositions with equivalent quantizations and encoding strategies for medical images. Proc SPIE. 1995;2431:571–582. doi: 10.1117/12.207655. [DOI] [Google Scholar]
- 8.Eckert MP. Lossy compression using wavelets, block DCT, and lapped orthogonal transforms optimized with a perceptual model. Proc SPIE. 1997;3031:339–350. doi: 10.1117/12.273912. [DOI] [Google Scholar]
- 9.Persons K, Palisson P, Manduca A, et al. An analytical look at the effects of compression on medical images. J Digit Imaging. 1997;10(suppl 1):60–66. doi: 10.1007/BF03168659. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mori T, Nakata H. Irreversible data compression in chest imaging using computed radiography: An evaluation. J Thorac Imaging. 1994;9:23–30. [PubMed] [Google Scholar]
- 11.Holbert JM, Staiger M, Chang TS, et al. Selection of algorithms for digital image compression: A rank-order study. Acad Radiol. 1995;2:273–276. doi: 10.1016/S1076-6332(05)80183-7. [DOI] [PubMed] [Google Scholar]
- 12.Kocher TE, Whiting B. Design considerations for a high-resolution film scanner for teleradiology applications. Proc SPIE. 1991;1446:459–464. doi: 10.1117/12.45305. [DOI] [Google Scholar]
- 13.Shapiro J. Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans Signal Processing. 1993;41:3445–3462. doi: 10.1109/78.258085. [DOI] [Google Scholar]
- 14.US Department of Justice, Federal Bureau of Investigation. WSQ gray-scale fingerprint image compression specification. IAFIS-IC-0110v2, 1993
- 15.Pennebaker WB, Mitchell JL. JPEG Still Image Data Compression Standard. New York, NY: Van Nostrand Reinhold; 1993. [Google Scholar]
- 16.Cox JE, Muka E, Wang X, et al. Factors affecting the selection of compression algorithms for projection radiography. Proc SPIE. 1997;3031:256–264. doi: 10.1117/12.273903. [DOI] [Google Scholar]