Lung Imaging and CADx (eBook, ePUB)

Redaktion: El-Baz, Ayman; Suri, Jasjit

• Format: ePub

Produktbeschreibung

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can significantly increase the patient's chance for survival. For this reason, CAD systems for lung cancer have been investigated in a large number of research studies.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 404
• Erscheinungstermin: 24. April 2019
• Englisch
• ISBN-13: 9780429508905
• Artikelnr.: 56504156

Autorenporträt

BIOGRAPHIES Ayman El-Baz is a professor, university scholar, and chair of the Bioengineering Department at the University of Louisville, Louisville, Kentucky. He earned his bachelor's and master's degrees in electrical engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, he was named a Coulter Fellow for his contributions to the field of biomedical translational research. He has 17 years of hands-on experience in the fields of bio-imaging modeling and noninvasive computer-assisted diagnosis systems. He has authored or coauthored more than 500 technical articles (132 journals, 23 books, 57 book chapters, 211 refereed-conference papers, 137 abstracts, and 27 U.S. patents and disclosures). Jasjit S. Suri is an innovator, scientist, a visionary, an industrialist, and an internationally known world leader in biomedical engineering. He has spent over 25 years in the field of biomedical engineering/devices and its management. He received his doctorate from the University of Washington, Seattle, and his business management sciences degree from Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio. He was awarded the President's Gold Medal in 1980 and named a Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions in 2004. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management.

Inhaltsangabe

Preface, Acknowledgement, Dedication. Chapter 1. Computer Aided Diagnosis
of COPD Using Accurate Lung Air Volume Estimation Using CT, Chapter 2.
Early detection of COPD: Influence on Lung Cancer Epidemiology, Chapter 3.
Dual Energy Computed Tomography for Lung Cancer Diagnosis and
Characterisation, Chapter 4. X-Ray Dark-Field Imaging of Lung Cancer in
Mice, Chapter 5. Lung Cancer Screening Using Low Dose Computed Tomography,
Chapter 6. Computer-aided diagnosis of lung nodules: systems for estimation
of lung cancer probability and false positive reduction of lung nodule,
Chapter 7. Automated Lung Cancer Detection From PET/CT Images Using Texture
and Fractal Descriptors,Chapter 8. Lung cancer risk of population exposed
to airborne particles: the contribution of different activities and
micro-environments, Chapter 9. Lung Nodule Classification based on the
Integration of Higher-Order MGRF Appearance Model and Geometric
Features,Chapter 10. Smoking cessation and lung cancer screening programs:
The rationale and method to integration,Chapter 11. Automatic Lung
Segmentation and Inter-observer Variability Analysis, Chapter 12.
Classification of Diseased Lungs using Combination of Riesz and Gabor
transforms with Machine Learning, Chapter 13. An Unsupervised Parametric
Mixture Model for Automatic Three Dimensional Lung Segmentation, Chapter
14. How Deep Learning is Changing the Landscape of Lung Cancer
Diagnosis?,Chapter 15. Early Assessment of Radiation Induced Lung Injury