Texture analysis imaging “what a clinical radiologist needs to know”

Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic...

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Bibliographic Details
Published in:European journal of radiology 2022-01, Vol.146, p.110055-110055, Article 110055
Main Authors: Corrias, Giuseppe, Micheletti, Giulio, Barberini, Luigi, Suri, Jasjit S, Saba, Luca
Format: Article
Language:English
Subjects:
Algorithms
Diagnostic Imaging
Humans
MRI
Pipeline
Radiogenomics
Radiography
Radiologists
Radiology
Radiomics
Texture analysis
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Summary:Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.
ISSN:0720-048X
1872-7727
DOI:10.1016/j.ejrad.2021.110055