Digital Compression in Medical Images

Alex Cazañas-Gordón; Esther Parra-Mora

Autores/as

Alex Cazañas-Gordón University of Coimbra https://orcid.org/0000-0002-4597-6328
Esther Parra-Mora University of Coimbra https://orcid.org/0000-0002-9008-031X

Palabras clave:

digital, compresión, imagenología, JPEG, DICOM

Resumen

La Imagenología desempeña un papel protagónico en el campo médico, siendo su uso ampliamente generalizado en el diagnóstico y tratamiento de numerosos trastornos de la salud.
Nuevos desarrollos en la adquisición de imágenes y en la tecnología de sensores hacen posible obtener imágenes más detalladas de varios órganos del cuerpo humano. Tal mejora es ciertamente ventajosa para la práctica médica, pero supone un encarecimiento de los recursos tecnológicos necesarios para manejar imágenes de alta resolución de manera confiable. Comúnmente, el manejo eficiente de medios digitales se apoya principalmente en la compresión digital. Desde un punto de vista técnico, las imágenes médicas podrían aprovechar las ventajas de la compresión digital. Sin embargo, peculiaridades de los datos médicos imponen restricciones a su uso. Este artículo presenta un vistazo a la compresión digital en el contexto de las imágenes médicas, y una breve discusión de los aspectos regulatorios y legales asociados a su uso.

Descargas

Los datos de descarga aún no están disponibles.

Biografía del autor/a

Alex Cazañas-Gordón, University of Coimbra

Alex Cazañas-Gordón received the B.E. degree in electrical engineering from the National Polytechnic School, Quito, Ecuador in 2003, and the MSc in Information Technology from the University of Queensland, Brisbane, Australia in 2015.
He is currently pursuing the Ph.D. degree in electrical and computer engineering at the University of Coimbra, Coimbra, Portugal.
Since 2018, he has been a researcher with the Multimedia Signal Processing Lab at the Department of Electrical and Computer Engineering of the University of Coimbra, Coimbra, Portugal. His research interests include signal processing, deep learning, optical coherence tomography, scanning laser ophthalmoscopy, and fundus photography.
Esther Parra-Mora, University of Coimbra

Esther Maria Parra-Mora received her bachelor’s degree in Electronics and Information Networks from the National Polytechnic School, Quito, Ecuador in 2007, and her master’s degree in Computer Science from The University of Queensland, Brisbane, Australia in 2015.
Since October 2017, she has been a Ph.D. student and researcher with the Department of Electrical and Computer Engineering at the University of Coimbra, Coimbra, Portugal. Her research focuses on automatic diagnosis of retinal diseases using deep learning techniques and diff erent modalities of retinal images.

Referencias

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.