Qualitative JPEG 2000 Compression in Digital Mammography – Evaluation Using 480 Mammograms of the CDMAM Phantom

Zusammenfassung

Ziel: Der DICOM-Standard unterstützt sowohl die quantitative als auch die qualitative lossy-Kompression von Mammografien. Ziel dieser Studie war, die qualitative JPEG-2000-lossy-Kompression zu untersuchen und herauszufinden, inwieweit unterschiedliche Faktoren wie Objektdicke, Röntgen-Dosis und lossy-Kompressionsstufen die Bildqualität beeinflussen. Material und Methoden: Das CDMAM Phantom Artinis 3.4 wurde mit 4 verschiedenen Objektdicken und 5 verschiedenen Röntgendosis geröntgt. Die resultierenden Bilder wurden mit 10 verschiedenen Kompressionsstufen komprimiert. Die Bildqualität wurde mit dem softwareinterpolierten IQFinv-Wert ermittelt. Ergebnisse: Lossy 90 führte zu 89 % Datenreduktion, lossy 70 zu 95 % und lossy 60 zu 96 %. Bei höheren Kompressionsstufen (lossy 30) reichte die resultierende Bildqualität von 80 – 36 %, bei niedrigen Kompressionsstufen (lossy 90) von 89 – 93 %. Die Objektdicke interagierte signifikant mit der Kompressionsstufe in Bezug zur resultierenden Bildqualität: höhere Kompressionsstufen führten zu zunehmend niedrigerer Bildqualität bei ansteigenden Kompressionsstufen (p < 0,05). Schlussfolgerung: Höhere qualitative JPEG-2000-Kompressionsstufen führen lediglich zu geringer zusätzlicher Datenreduktion, während die resultierende Bildqualität nicht mehr verlässlich vorausgesagt werden kann. Faktoren, die die Bildqualität beeinflussen wie Objektdicke und Röntgendosis, sollten bei der Bildkompression berücksichtigt werden. Große Objektdicken sollten mit Vorsicht komprimiert werden, weil der Verlust an Bildqualität größer zunehmend größer wird, wenn qualitative Kompressionsalgorithmen verwendet werden.

Abstract

Purpose: The DICOM standard supports both quantitative and qualitative lossy compression of mammograms.The purpose of this study was to investigate qualitative JPEG 2000 lossy compression and how different factors such as object thickness, radiation dose, and lossy compression levels affect image quality. Materials and Methods: The CDMAM phantom Artinis 3.4 was radiographed with 4 different object thicknesses and 5 different doses. The images were compressed at 10 different compression levels. The image quality was assessed by the software interpolated IQFinv value. Results: Lossy 90 resulted in 89 % data reduction, lossy 70 in 95 % data reduction and lossy 60 in 96 % data reduction. At higher compression levels (lossy 30), the resulting image quality ranged from 80 – 36 %, and at low compression levels (lossy 90), it ranged from 89 – 93 %. The object thickness was found to significantly interact with the compression level with regard to the resulting image quality: a higher object thickness resulted in increasingly poor image quality at increasing compression levels (p < 0.05). Conclusion: Higher qualitative JPEG 2000 compression levels contribute only little additional data reduction, while the resulting image quality cannot be reliably predicted. Factors affecting image quality such as radiation dose and object thickness should be taken into account when performing image compression. Large object thicknesses should be compressed with caution because the loss of image quality is greater when intelligent data compression algorithms are used.

breast - mammography - PACS

References

• 1 Bick U, Diekmann F. Digital mammography: what do we and what don’t we know?.  Eur Radiol. 2007;  17 1931-1942
• 2 Van Ongeval C. Digital mammography for screening and diagnosis of breast cancer: an overview.  Jbr-Btr. 2007;  90 163-166
• 3 Kamitani T, Yabuuchi H, Soeda H et al. Detection of masses and microcalcifications of breast cancer on digital mammograms: comparison among hard-copy film, 3-megapixel liquid crystal display (LCD) monitors and 5-megapixel LCD monitors: an observer performance study.  Eur Radiol. 2007;  17 1365-1371
• 4 Vigeland E, Klaasen H, Klingen T A et al. Full-field digital mammography compared to screen film mammography in the prevalent round of a population-based screening programme: the Vestfold County Study.  Eur Radiol. 2008;  18 183-191
• 5 Fischer U, Hermann K P, Baum F. Digital mammography: current state and future aspects.  Eur Radiol. 2006;  16 38-44
• 6 Schulz-Wendtland R, Hermann K P, Wacker T et al. Current situation and future perspectives of digital mammography.  Radiologe. 2008;  48 324-334
• 7 Zheng B, Sumkin J H, Good W F et al. Applying computer-assisted detection schemes to digitized mammograms after JPEG data compression: an assessment.  Acad Radiol. 2000;  7 595-602
• 8 Chan H P, Lo S C, Niklason L T et al. Image compression in digital mammography: effects on computerized detection of subtle microcalcifications.  Med Phys. 1996;  23 1325-1336
• 9 Shiao Y H, Chen T J, Chuang K S et al. Quality of compressed medical images.  J Digit Imaging. 2007;  20 149-159
• 10 Slone R M, Foos D H, Whiting B R et al. Assessment of visually lossless irreversible image compression: comparison of three methods by using an image-comparison workstation.  Radiology. 2000;  215 543-553
• 11 Erickson B J. Irreversible compression of medical images.  J Digit Imaging. 2002;  15 5-14
• 12 Loose R, Braunschweig R, Kotter E et al. Compression of digital images in radiology – results of a consensus conference.  Fortschr Röntgenstr. 2009;  181 32-37
• 13 Koff D, Bak P, Brownrigg P et al. Pan-Canadian Evaluation of Irreversible Compression Ratios (”Lossy” Compression) for Development of National Guidelines.  J Digit Imaging. 2009;  22 569-578
• 14 Lee K H, Kim Y H, Kim B H et al. Irreversible JPEG 2000 compression of abdominal CT for primary interpretation: assessment of visually lossless threshold.  Eur Radiol. 2007;  17 1529-1534
• 15 Savcenko V, Erickson B J, Persons K R et al. An evaluation of JPEG and JPEG 2000 irreversible compression algorithms applied to neurologic computed tomography and magnetic resonance images. Joint Photographic Experts Group.  J Digit Imaging. 2000;  13 183-185
• 16 Sung M M, Kim H J, Yoo S K et al. Clinical evaluation of compression ratios using JPEG2000 on computed radiography chest images.  J Digit Imaging. 2002;  15 78-83
• 17 Kotter E, Roesner A, Torsten Winterer J et al. Evaluation of Lossy data compression of chest X-rays: a receiver operating characteristic study.  Invest Radiol. 2003;  38 243-249
• 18 Liang Z, Du X, Liu J et al. Effects of different compression techniques on diagnostic accuracies of breast masses on digitized mammograms.  Acta Radiol. 2008;  49 747-751
• 19 Penedo M, Lado M J, Tahoces P G et al. Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms.  IEEE Trans Inf Technol Biomed. 2006;  10 354-361
• 20 Suryanarayanan S, Karellas A, Vedantham S et al. Detection of simulated lesions on data-compressed digital mammograms.  Radiology. 2005;  236 31-36
• 21 Wollenweber T, Freund M. Is telemammography an option for screening programs? A phantom study.  Fortschr Röntgenstr. 2005;  177 1284-1289
• 22 Penedo M, Souto M, Tahoces P G et al. Free-response receiver operating characteristic evaluation of lossy JPEG2000 and object-based set partitioning in hierarchical trees compression of digitized mammograms.  Radiology. 2005;  237 450-457
• 23 Suryanarayanan S, Karellas A, Vedantham S et al. A perceptual evaluation of JPEG 2000 image compression for digital mammography: contrast-detail characteristics.  J Digit Imaging. 2004;  17 64-70
• 24 Mayo P, Rodenas F, Verdu G et al. Study of digital mammographic equipments by phantom image quality.  Conf Proc IEEE Eng Med Biol Soc. 2006;  1 1994-1996
• 25 Mayo P, Rodenas F, Verdu G et al. Analysis of digital image quality indexes for CIRS SP 01 and CDMAM 3.4 mammographic phantoms.  Conf Proc IEEE Eng Med Biol Soc. 2008;  2008 418-421
• 26 Last access. http://”www.rcr.ac.uk/docs/radiology/pdf/IT_guidance_lossyApr08.pdf” 12.02.2011
• 27 Li Y, Poulos A, McLean D et al. A review of methods of clinical image quality evaluation in mammography.  Eur J Radiol. 2010;  74 122-131
• 28 Baldelli P, Phelan N, Egan G. A novel method for contrast-to-noise ratio (CNR) evaluation of digital mammography detectors.  Eur Radiol. 2009;  19 2275-2285
• 29 Goes C E, Schiabel H, Nunes F L. Evaluation of microcalcifications segmentation techniques for dense breast digitized images.  J Digit Imaging. 2002;  15 231-233
• 30 Veldkamp W J, Kroft L J, Delft J P et al. A technique for simulating the effect of dose reduction on image quality in digital chest radiography.  J Digit Imaging. 2009;  22 114-125
• 31 Yakabe van M, Sakai S, Yabuuchi H et al. Effect of Dose Reduction on the Ability of Digital Mammography to Detect Simulated Microcalcifications.  J Digit Imaging. 2010;  23 520-526
• 32 Kropil P, Cohnen M, Andersen K et al. Image quality in multidetector CT of paranasal sinuses: potential of dose reduction using an adaptive post-processing filter.  Fortschr Röntgenstr. 2010;  182 973-978
• 33 Kropil P, Lanzman R S, Walther C et al. Dose reduction and image quality in MDCT of the upper abdomen: potential of an adaptive post-processing filter.  Fortschr Röntgenstr. 2010;  182 248-253
• 34 Nagel H D, Stumpp P, Kahn T et al. Performance of an automatic dose control system for CT: specifications and basic phantom tests.  Fortschr Röntgenstr. 2011;  183 60-67
• 35 Pfandzelter R, Wulfing U, Boedeker B et al. Diagnostic image quality of mammograms in german outpatient medical care.  Fortschr Röntgenstr. 2010;  182 993-1000
• 36 Sommer A, Girnus R, Wendt B et al. Devolvement of an objective rating system for the annual physical quality control for digital mammography systems.  Fortschr Röntgenstr. 2010;  182 788-792
• 37 Marshall N W. A comparison between objective and subjective image quality measurements for a full field digital mammography system.  Phys Med Biol. 2006;  51 2441-2463
• 38 Chakrabarti K, Kaczmarek R V, Thomas J A et al. Effect of room illuminance on monitor black level luminance and monitor calibration.  J Digit Imaging. 2003;  16 350-355
• 39 Blendl C, Loos C, Eiben B. Comparison of two automatic evaluation methods on Images of the CDMAM test phantom.  Fortschr Röntgenstr. 2009;  181 637-643
• 40 Blendl C, Schreiber A C, Buhr H. Results of an automatic evaluation of test images according to PAS 1054 and IEC 6220-1 – 2 on different types of digital mammographic units.  Fortschr Röntgenstr. 2009;  181 979-988
• 41 Sommer A, Lenzen H, Blaser D et al. Guideline for the additional test positions according to the EPQC 4^th Edition for Digital Mammography Systems.  Fortschr Röntgenstr. 2009;  181 845-850
• 42 Van Ongeval C, Van Steen A, Geniets C et al. Clinical image quality criteria for full field digital mammography: a first practical application.  Radiat Prot Dosimetry. 2008;  129 265-270
• 43 Thomas J A, Chakrabarti K, Kaczmarek R et al. Contrast-detail phantom scoring methodology.  Med Phys. 2005;  32 807-814
• 44 Andersson I, Ikeda D M, Zackrisson S et al. Breast tomosynthesis and digital mammography: a comparison of breast cancer visibility and BIRADS classification in a population of cancers with subtle mammographic findings.  Eur Radiol. 2008;  18 2817-2825
• 45 Diekmann F, Bick U. Tomosynthesis and contrast-enhanced digital mammography: recent advances in digital mammography.  Eur Radiol. 2007;  17 3086-3092
• 46 Teertstra H J, Loo C E, Bosch M A et al. Breast tomosynthesis in clinical practice: initial results.  Eur Radiol. 2010;  20 16-24
• 47 Bajpai van den V, Lee K H, Kim B et al. Differences in compression artifacts on thin- and thick-section lung CT images.  Am J Roentgenol. 2008;  191 W38-W43