Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Author:   Michael T. McCann ,  Michael Unser
Publisher:   now publishers Inc
ISBN:  

9781680836509


Pages:   88
Publication Date:   03 December 2019
Format:   Paperback
Availability:   In Print   Availability explained
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Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks


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Overview

Biomedical imaging is a vast and diverse field. There are a plethora of imaging devices using light, X-rays, sound waves, magnetic fields, electrons, or protons, to measure structures ranging from nano to macroscale. In many cases, computer software is needed to turn the signals collected by the hardware into a meaningful image. These computer algorithms are similarly diverse and numerous.This survey presents a wide swath of biomedical image reconstruction algorithms under a single framework. It is a coherent, yet brief survey of some six decades of research. The underpinning theory of the techniques are described and practical considerations for designing reconstruction algorithms for use in biomedical systems form the central theme of each chapter. The unifying framework deployed throughout the monograph models imaging modalities as combinations of a small set of building blocks, which identify connections between modalities. Thus, the user can quickly port ideas and computer code from one to the next. Furthermore, reconstruction algorithms can treat the imaging model as a black. box, meaning that one algorithm can work for many modalities. This provides a pragmatic approach to designing effective reconstruction algorithms.This monograph is written in a tutorial style that concisely introduces students, researchers and practitioners to the development and design of effective biomedical image reconstruction algorithms.

Full Product Details

Author:   Michael T. McCann ,  Michael Unser
Publisher:   now publishers Inc
Imprint:   now publishers Inc
Weight:   0.137kg
ISBN:  

9781680836509


ISBN 10:   1680836501
Pages:   88
Publication Date:   03 December 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

1. Introduction 2. Forward Models 3. Classical Image Reconstruction 4. Sparsity-Based Image Reconstruction 5. The Learning (R)Evolution 6. Conclusion Acknowledgements References

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