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Artificial Intelligence Review

28.06.2016

Image descriptors in radiology images: a systematic review

verfasst von: Mariana A. Nogueira, Pedro Henriques Abreu, Pedro Martins, Penousal Machado, Hugo Duarte, João Santos

Erschienen in: Artificial Intelligence Review | Ausgabe 4/2017

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Abstract

Clinical decisions are sometimes based on a variety of patient’s information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors—computational algorithms that present a summarized description of image regions—became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures—e.g., intensity, texture, shape—in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer’s disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.

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Titel: Image descriptors in radiology images: a systematic review
verfasst von: Mariana A. Nogueira
Pedro Henriques Abreu
Pedro Martins
Penousal Machado
Hugo Duarte
João Santos
Publikationsdatum: 28.06.2016
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 4/2017
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-016-9492-8

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