Digital Compression in Medical Images

Keywords: digital, compression, medical, imaging, JPEG, DICOM

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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Author Biographies

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.

Published
2022-01-03
Section
Research Articles for the Regular Issue

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