Computational Medicine in Radiology: Medical Images as Big Data

Abstract

Radiology has been transformed in recent decades from a specialty that relied on subjective interpretation of qualitative assessment of medical images to one that is now informed by objective analyses of medical images as quantitative constructs through advanced computational methods. This paper discusses medical images as mathematical data structures, from the pipeline of physics and engineering through which medical images are acquired to the unique attributes of images obtained from patients in a clinical setting to post-acquisition image processing that leads to computer-aided diagnosis (now dominated by artificial intelligence systems). The magnitude of data contained within images from a single imaging study and the volume of imaging studies stored in a clinical archive or required for research clearly warrant the label “big data.”

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