ABSTRACT
Computerized medical image interpretation dates back to the 1960’s, although the field gained much momentum in the 1990’s. Since medical images were made available in digital format, initially through digitalization of screen films and later through direct digital acquisition, computerized image processing and visualization techniques have been used to achieve more accurate, reliable and faster image interpretation. The availability of deep learning has changed the medical imaging landscape by enabling learning directly from data through general purpose learning algorithms. Quantitative Image Analysis refers to the extraction of quantifiable features from medical images for the assessment of the presence, severity, degree of change, or status of a disease, injury, or chronic condition, compared to the normal, healthy status. The concept that biomedical images contain information that reflects the underlying pathophysiology, and that the relationships can be revealed via quantitative image analyses, motivates the development of the growing radiomics field.
