Determining optimal medical image compression: psychometric and image distortion analysis

BMC Medical Imaging

Alexander C Flint¹ 

[1] Interconnect Medical, LLC, Menlo Park, CA, 94025, USA

关键词: Image analysis;    Psychometrics;    Lossy;    JPEG;    Medical image compression;   

Others  :  1091847
DOI  :  10.1186/1471-2342-12-24

 received in 2012-03-29, accepted in 2012-07-17,  发布年份 2012

【 摘 要 】

Background

Storage issues and bandwidth over networks have led to a need to optimally compress medical imaging files while leaving clinical image quality uncompromised.

Methods

To determine the range of clinically acceptable medical image compression across multiple modalities (CT, MR, and XR), we performed psychometric analysis of image distortion thresholds using physician readers and also performed subtraction analysis of medical image distortion by varying degrees of compression.

Results

When physician readers were asked to determine the threshold of compression beyond which images were clinically compromised, the mean image distortion threshold was a JPEG Q value of 23.1 ± 7.0. In Receiver-Operator Characteristics (ROC) plot analysis, compressed images could not be reliably distinguished from original images at any compression level between Q = 50 and Q = 95. Below this range, some readers were able to discriminate the compressed and original images, but high sensitivity and specificity for this discrimination was only encountered at the lowest JPEG Q value tested (Q = 5). Analysis of directly measured magnitude of image distortion from subtracted image pairs showed that the relationship between JPEG Q value and degree of image distortion underwent an upward inflection in the region of the two thresholds determined psychometrically (approximately Q = 25 to Q = 50), with 75 % of the image distortion occurring between Q = 50 and Q = 1.

Conclusion

It is possible to apply lossy JPEG compression to medical images without compromise of clinical image quality. Modest degrees of compression, with a JPEG Q value of 50 or higher (corresponding approximately to a compression ratio of 15:1 or less), can be applied to medical images while leaving the images indistinguishable from the original.

【 授权许可】

   
2012 Flint; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Schreiber D: Swelling archives warrant closer look at compression. Radiol Manage 2003, 25:36-39.
• [2]Erickson BJ: Irreversible compression of medical images. J Digit Imaging 2002, 15:5-14.
• [3]Brennan PC, McEntee M, Evanoff M, Phillips P, O’Connor WT, Manning DJ: Ambient Lighting: Effect of Illumination on Soft-Copy Viewing of Radiographs of the Wrist. American Journal of Roentgenology 2007, 188:W177-W180.
• [4]Chawla AS, Samei E: Ambient illumination revisited: a new adaptation-based approach for optimizing medical imaging reading environments. Med Phys 2007, 34:81-90.
• [5]Fidler A, Likar B, Skaleric U: Lossy JPEG compression: easy to compress, hard to compare. Dentomaxillofac Radiol 2006, 35:67-73.
• [6]Fidler A, Skaleric U, Likar B: The impact of image information on compressibility and degradation in medical image compression. Med Phys 2006, 33:2832-2838.
• [7]Fidler A, Skaleric U, Likar B: The effect of image content on detail preservation and file size reduction in lossy compression. Dentomaxillofac Radiol 2007, 36:387-392.
• [8]Braunschweig R, Kaden I, Schwarzer J, Sprengel C, Klose K: Image data compression in diagnostic imaging: international literature review and workflow recommendation. Rofo 2009, 181:629-636.
• [9]Loose R, Braunschweig R, Kotter E, Mildenberger P, Simmler R, Wucherer M: Compression of digital images in radiology - results of a consensus conference. Rofo 2009, 181:32-37.
• [10]Koff DA, Shulman H: An overview of digital compression of medical images: can we use lossy image compression in radiology? Can Assoc Radiol J 2006, 57:211-217.
• [11]Koff D, Bak P, Brownrigg P, Hosseinzadeh D, Khademi A, Kiss A, Lepanto L, Michalak T, Shulman H, Volkening A: Pan-Canadian evaluation of irreversible compression ratios (“lossy” compression) for development of national guidelines. J Digit Imaging 2009, 22:569-578.
• [12]Kang BJ, Kim HS, Park CS, Choi JJ, Lee JH, Choi BG: Acceptable compression ratio of full-field digital mammography using JPEG 2000. Clin Radiol 2011, 66:609-613.
• [13]Kim KJ, Kim B, Lee KH, Kim TJ, Mantiuk R, Kang H-S, Kim YH: Regional difference in compression artifacts in low-dose chest CT images: effects of mathematical and perceptual factors. AJR Am J Roentgenol 2008, 191:W30-37.
• [14]Kim KJ, Lee KH, Kim B, Richter T, Yun ID, Lee SU, Bae KT, Shim H: JPEG2000 2D and 3D reversible compressions of thin-section chest CT images: improving compressibility by increasing data redundancy outside the body region. Radiology 2011, 259:271-277.
• [15]Kim TJ, Lee KH, Kim B, Kim KJ, Chun EJ, Bajpai V, Kim YH, Hahn S, Lee KW: Regional variance of visually lossless threshold in compressed chest CT images: lung versus mediastinum and chest wall. Eur J Radiol 2009, 69:483-488.