Lung Imaging and CADx

DESCRIPTION

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

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).

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