Lung Imaging and CADx

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.

    Overviews the latest state-of-the-art diagnostic CAD systems for lung cancer imaging and diagnosis

    Offers detailed coverage of 3D and 4D image segmentation

    Illustrates unique fully automated detection systems coupled with 4D Computed Tomography (CT)

    Written by authors who are world-class researchers in the biomedical imaging sciences

    Includes extensive references at the end of each chapter to enhance further study

Table of Contents

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

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.