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. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This book overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps.
Ayman El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor’s and master’s degree 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, Dr. El-Baz was named a Coulter Fellow for his
contributions to the field of biomedical translational research. He has 15 years of hands-on experience in the fields of bio-imaging modeling and noninvasive computer-assisted diagnosis systems and has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents).
Jasjit S. Suri is an innovator, scientist, visionary, industrialist, and internationally known leader in Biomedical Engineering. He has spent over 24 years in the field of biomedical engineering/devices and its management. He received his Doctorate from the University of Washington, Seattle and his MBA in Business Management Sciences from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr. Suri was honored with the Director General’s Gold medal in 1980 for his
outstanding contributions and is a Fellow of the American Institute of Medical and Biological Engineering
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