Medical Image File Formats

Abstract

Image file format is often a confusing aspect for someone wishing to process medical images. This article presents a demystifying overview of the major file formats currently used in medical imaging: Analyze, Neuroimaging Informatics Technology Initiative (Nifti), Minc, and Digital Imaging and Communications in Medicine (Dicom). Concepts common to all file formats, such as pixel depth, photometric interpretation, metadata, and pixel data, are first presented. Then, the characteristics and strengths of the various formats are discussed. The review concludes with some predictive considerations about the future trends in medical image file formats.

Assessing Reproducibility (In The Practice of Reproducible Research Case Studies and Lessons from the Data-Intensive Sciences Justin Kitzes, Daniel Turek, Fatma Deniz (Eds.))

“…In addition, there are also open data formats that have been developed in specifi c research communities to properly store data and metadata relevant to the analysis of data from this research domain. Examples include the FITS data format for astronomical data (Wells et al, 1981), and the NIFTI and DICOM fi le formats for medical imaging data (Larobina & Murino, 2014).…”

Medical high-resolution image sharing and electronic whiteboard system: A pure-web-based system for accessing and discussing lossless original images in telemedicine

“…These requirements give rise to two technical problems: how to rapidly access lossless medical HRIs in a diversified network environment and in different client devices without installing special software, and how to realize convenient and effective communication online among different client devices. For rapidly accessing medical HRIs, including DICOM (Digital Imaging and Communications in Medicine) [7], clinicians always try to archive high-quality medical images [8,9], however, there is much limited bandwidth in some remote areas of developing countries and an interface bottleneck in some areas of different countries [10] where HRIs sharing and discussion are most required. Therefore, lossless and rapid sharing of the HRIs is becoming more important.…”

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

“…Although less common, image data may also be real numbers. The Institute of Electrical and Electronics Engineers created a standard (IEEE-754) which defines two basic formats for the encoding in binary of floating-point numbers: the single precision 32-bit and the double precision 64-bit [30]. CT datasets are filled with noise created by the capturing instrument, X-rays, beam detectors, and onboard electronics all contribute noise to the resulting images.…”

“…A data set generated from a CT technology is typically comprised of transformations of the raw CT numbers, which are saved in standard medical image format with 8 bits, 16 bits or 32 bits for each voxel [30]. (Recall that 1 byte is equal to 8 bits).…”