Digital Compression in Medical Images
Abstract
Imaging technology has long played a principal role in the medical domain, and as such, its use is widespread in the diagnosis and treatment of numerous health conditions. Concurrently, new developments in imaging techniques and sensor technology make possible the acquisition of increasingly detailed images of several organs of the human body. This improvement is indeed advantageous for medical practitioners. However, it comes to a cost in the form of storage and telecommunication infrastructures needed to handle high-resolution images reliably. Ordinarily, digital compression is a mainstay in the efficient management of digital media, including still images and video. From a technical point of view, medical imaging could take full advantage of digital compression technology. However, nuances unique to medical data impose constraints to the application of digital compression in medical images. This paper presents an overview of digital compression in the context of still medical images, along with a brief discussion on related regulatory and legal implications.
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References
R. Duszak, "Medical imaging: is the growth boom over," in "Neiman Report, Harvey L. Neiman Health Policy Institute, Reston, Virginia," 2012.
R. Zhang et al., "Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: Value of artificial intelligence," Radiology, vol. 298, no. 2, pp. E88-E97, 2021.
Y. Xie et al., "Early lung cancer diagnostic biomarker discovery by machine learning methods," Translational oncology, vol. 14, no. 1, p. 100907, 2021.
G. Abate et al., "A conformation variant of p53 combined with machine learning identifies Alzheimer disease in preclinical and prodromal stages," Journal of personalized medicine, vol. 11, no. 1, p. 14, 2021.
X. Tang et al., "Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas," The American Journal of Pathology, vol. 191, no. 3, pp. 454-462, 2021.
R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, "Prediction of heart disease using a combination of machine learning and deep learning," Computational Intelligence and Neuroscience, vol. 2021, 2021.
E. Parra-Mora, A. Cazañas-Gordon, R. Proença, and L. A. da Silva Cruz, "Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning," IEEE Access, vol. 9, pp. 99201-99219, 2021.
A. Nait-Ali and C. Cavaro-Menard, Compression of Biomedical Images and Signals. Wiley-IEEE Press, 2008.
S. S. Parikh, D. Ruiz, H. Kalva, G. Fernández-Escribano, and V. Adzic, "High bit-depth medical image compression with hevc," IEEE journal of biomedical and health informatics, vol. 22, no. 2, pp. 552-560, 2017.
V. Sanchez, F. Auli-Llinas, R. Vanam, and J. Bartrina-Rapesta, "Rate control for lossless region of interest coding in HEVC intra-coding with applications to digital pathology images," in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015: IEEE, pp. 1250-1254.
N. M. Banu and S. Sujatha, "3D medical image compression: a review," Indian Journal of Science and technology, vol. 8, no. 12, p. 1, 2015.
H. Oh, A. Bilgin, and M. W. Marcellin, "Visually lossless encoding for JPEG2000," IEEE Transactions on Image Processing, vol. 22, no. 1, pp. 189-201, 2012.
F. Valente, L. A. B. Silva, T. M. Godinho, and C. Costa, "Anatomy of an extensible open source PACS," Journal of digital imaging, vol. 29, no. 3, pp. 284-296, 2016.
I. C. Stoica, S. Mogos, A. Draghici, and R. Cergan, "The medical and medicolegal use of the radiological image storage PACS for an orthopedic hospital," Rom J Leg Med, vol. 25, pp. 235-238, 2017.
L. Yan, "DICOM standard and Its Application in PACS system," Medical Imaging Process & Technology, vol. 1, no. 1, 2018.
A. Cazañas-Gordón and E. Parra-Mora, "The Internet of Things in Healthcare. An Overview," Latin-American Journal of Computing, vol. 7, no. 1, pp. 86-99, 2020.
National Electrical Manufacturers Association (NEMA). "PS3.5 2016b—Data Structures and Encoding." https://bit.ly/3rEs2Fj (accessed accessed on July 25th 2018, 2018).
Wikimedia Commons. "File: CT of rectus sheath hematomas.png." Online. https://bit.ly/3BeFtyO (accessed 18-oct, 2021).
Wikimedia Commons. "File:Ultrasound abdomen - liver cirrhosis - 10.jpg - Wikimedia Commons." Online. https://bit.ly/34KuZex (accessed 17-October, 2021).
W. A. Pearlman and A. Said, "Digital Signal Compression: principles and practice," Cambridge University Press, 2011, pp. 251-252.
J. Kivijärvi, T. Ojala, T. Kaukoranta, A. Kuba, L. Nyúl, and O. Nevalainen, "A comparison of lossless compression methods for medical images," Computerized Medical Imaging and Graphics, vol. 22, no. 4, pp. 323-339, 1998.
M.-M. Sung et al., "Clinical Evaluation of Compression Ratios using JPEG2000 on Computed Radiography Chest Images," Journal of Digital Imaging, vol. 15, no. 2, pp. 78-83, 2002, doi: 10.1007/s10278-002-0007-6.
DICOM Standards Committee. Working Group 4 Compression, "Supplement 61:“JPEG2000 transfer syntaxes," 2009.
J. T. Norweck et al., "ACR–AAPM–SIIM technical standard for electronic practice of medical imaging," Journal of digital imaging, vol. 26, no. 1, pp. 38-52, 2013.
The Royal College of Radiologists. "The adoption of lossy image data compression for the purpose of clinical interpretation." https://bit.ly/3Jmw1fE. (accessed 12-07-2020, 2020).
R. Loose, R. Braunschweig, E. Kotter, P. Mildenberger, R. Simmler, and M. Wucherer, "Kompression digitaler Bilddaten in der Radiologie–Ergebnisse einer Konsensuskonferenz," in RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2009, vol. 181, no. 01: © Georg Thieme Verlag KG Stuttgart· New York, pp. 32-37.
The Royal Australian and New Zealand College of Radiologists. "Guideline for the Use of Image Compression in Diagnostic Imaging." https://bit.ly/34Q2bkG (accessed 12-07-2021, 2021).
M. Abràmoff and C. N. Kay, "Chapter 6 - Image Processing," in Retina, vol. 1, S. J. Ryan et al. Eds., 5th ed. London: W.B. Saunders, 2013, pp. 151-176.
D. Dennison and K. Ho, "Informatics Challenges—Lossy Compression in Medical Imaging," Journal of Digital Imaging, vol. 27, no. 3, pp. 287-291, 2014, doi: 10.1007/s10278-014-9693-0.
European Society of Radiology (ESR), "Usability of irreversible image compression in radiological imaging. A position paper by the European Society of Radiology (ESR)," Insights into Imaging, journal article vol. 2, no. 2, pp. 103-115, April 01 2011, doi: 10.1007/s13244-011-0071-x.
J. P. Fritsch and R. Brennecke, "Lossy JPEG Compression in Quantitative Angiography: the Role of X-ray Quantum Noise," Journal of Digital Imaging, vol. 24, no. 3, pp. 516-527, 2011, doi: 10.1007/s10278-010-9275-8.
A. Fidler, B. Likar, and U. Skalerič, "Lossy JPEG compression: easy to compress, hard to compare," Dentomaxillofacial Radiology, vol. 35, no. 2, pp. 67-73, 2006, doi: 10.1259/dmfr/52842661.
D. A. Koff and H. Shulman, "An overview of digital compression of medical images: can we use lossy image compression in radiology?," Canadian Association Of Radiologists Journal, vol. 57, no. 4, pp. 211-217, 2006.
F. Liu, M. Hernandez-Cabronero, V. Sanchez, M. W. Marcellin, and A. Bilgin, "The Current Role of Image Compression Standards in Medical Imaging," Information, vol. 8, no. 4, p. 131, 2017.
D. M. Chandler, N. L. Dykes, and S. S. Hemami, "Visually lossless compression of digitized radiographs based on contrast sensitivity and visual masking," in Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, 2005, vol. 5749: International Society for Optics and Photonics, pp. 359-373.
Y. Zhang, Z. Dong, L. Wu, S. Wang, and Z. Zhou, "Feature Extraction of Brain MRI by Stationary Wavelet Transform," in 2010 International Conference on Biomedical Engineering and Computer Science, 23-25 April 2010 2010, pp. 1-4, doi: 10.1109/ICBECS.2010.5462491.
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