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Multi-modality Feature Learning in Diagnoses of Alzheimer’s Disease Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Radiomics in Medical Imaging—Detection, Extraction and Segmentation

Authors : Jie Tian, Di Dong, Zhenyu Liu, Yali Zang, Jingwei Wei, Jiangdian Song, Wei Mu, Shuo Wang, Mu Zhou

Published in: Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Radiomics, as a newly emerging technology, converts medical images into high-dimensional data via high-throughput extraction of quantitative features, followed by subsequent data analysis for decision support. It identifies general diagnostic or prognostic phenotypes with target clinical need, providing an unprecedented opportunity to improve individualized treatment in cancer at low cost. In this chapter, we will introduce radiomics from its development to its clinical applications. We divide the clinical applications into three sections based on three most common medical modality, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET), to give a comprehensive introduction of how radiomics works with the example of a typical cancer type. The workflow and detailed technology skills are well described in each section.

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previous chapter Computer-Aided Prognosis: Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multi-modal MRI Analysis
next chapter Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques
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Metadata
Title
Radiomics in Medical Imaging—Detection, Extraction and Segmentation
Authors
Jie Tian
Di Dong
Zhenyu Liu
Yali Zang
Jingwei Wei
Jiangdian Song
Wei Mu
Shuo Wang
Mu Zhou
Copyright Year
2018
Book
Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Print ISBN: 978-3-319-68842-8

Electronic ISBN: 978-3-319-68843-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-68843-5_11

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