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Lung Cancer and Imaging

Ayman El-Baz, Jasjit S Suri

Description

Lung cancer is one of the most common cancers in men and women worldwide. Early diagnosis of lung cancer can significantly increase the chances of a patient's survival, yet early detection has historically been difficult. As a result, there has been a great deal of progress in the development of accurate and fast diagnostic tools in recent years. Lung Cancer and Imaging provides an introduction to the methods currently used in lung cancer diagnosis and the promising new techniques that are emerging. Areas covered include the major trends and challenges in lung cancer detection and diagnosis, classification of cancer types, lung feature extraction in joint PET/CT images, and algorithms in the area of low dosage CT lung cancer images.

About Editors

Ayman El-Baz is a professor, university scholar, and the chair of the Bioengineering Department at the University of Louisville, Kentucky. El-Baz has 17 years of hands-on experience in the fields of bioimaging modeling and non-invasive computer-assisted diagnosis systems. In 2009, he was named a Coulter Fellow for his contributions to the field of biomedical translational research. He has authored or co-authored more than 500 technical articles across journals, books, book chapters, conference papers, abstracts, and US patents and disclosures.

Jasjit S Suri is an innovator, scientist, industrialist and an internationally known world leader in biomedical engineering. He has spent more than 25 years in the field of biomedical engineering/devices. In 2018, he was awarded the Marquis Lifetime Achievement Award for his outstanding contributions and dedication to medical imaging and its management.

Table of Contents

Preface

Acknowledgement

Dedication

1. Early Diagnosis System for Lung Nodules Based on The Integration of Higher-Order MGRF Appearance Feature Model and 3DCNN
Ahmed Shaffie, Ahmed Soliman, Ali Mahmoud, Hadil Abu Khalifeh, Fatma Taher, Mohammed Ghazal, Adel Elmaghraby, and Ayman El-Baz

2. Capsule Networks for Lung Cancer Screening
Aryan Mobiny, Supratik Moulik, Naveen Garg, Carol C. Wu, and Hien V. Nguyen

3. Quantitative Malignancy Recognition of Lung Cancer Using Non-Invasive Image Modalities
Chung-Ming Lo

4. Epidemiology of Lung Cancer
Meng-Hua Tao

5. Use of Biomarkers in Lung Cancer Diagnosis, Prognosis and Treatment
Saima Shakil Malik, Nosheen Masood, Iqra

6. Deep Learning for Medical Image Processing: Bones and Soft Tissue Separation in Chest Radiographs
Amin Zarshenas, Kenji Suzuki

7. Radiomics and Lung Cancer: Promising News for Early Detected Nodules
Stefania Rizzo, Filippo Del Grande, Francesco Petrella

8. Photodynamic Diagnosis and Treatment of Lung Cancer
Anine Crous, Heidi Abrahamse

9. Cold Atmospheric Plasma and Iron Oxide-Based Magnetic Nanoparticles for Synergetic Lung Cancer Therapy
Hongli Yu, Wentong Li, Weifen Zhang

10. Exploiting Exhaled Aerosol Fingerprints to Detect Lung Cancers and Obstructive Respiratory Diseases
Jinxiang Xi, Xiuhua April Si

11. A Study of Ground-Glass Opacity (GGO) Nodules in Automated Detection of Lung Cancer
May Phu Paing, Chuchart Pintavirooj, Kazuhiko Hamamoto, Supan Tungjitkusolmun

12. Electromagnetic Imaging and Lung Ablation
Lulu Wang

Bibliographic

Hardback ISBN: 9780750325387

Ebook ISBN: 9780750325400

DOI: 10.1088/978-0-7503-2540-0

Publisher: Institute of Physics Publishing

Series: IPEM-IOP Series in Physics and Engineering in Medicine and Biology

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